# Image Image refers to a depiction or representation that visually portrays objects, scenes, or ideas. This topic covers a broad spectrum from digital photographs and illustrations to complex graphics used in various forms of media. It explores the creation, editing, and usage of images in fields like photography, graphic design, advertising, and digital art. This topic also delves into the technologies and software that facilitate image processing and enhancement, highlighting trends and innovations in visual media. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 25 Places Designers Can Still Find Free, Non-AI Images for Commercial Use DevFeed: [25 Places Designers Can Still Find Free, Non-AI Images for Commercial Use]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-17T17:00:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Image](), [Web](), [Artificial Intelligence]() Tags: [ai](), [ai-images](), [asset](), [cc0-images](), [commercial-use-images](), [creative-commons](), [design-resources](), [free-design-assets](), [free-images-for-designers](), [free-stock-photos](), [human-made-images](), [image-licensing](), [images](), [museum-images](), [non-ai-images](), [open-access-images](), [pexels](), [photography](), [photography-resources](), [pixabay](), [public-domain-images](), [stock-photography](), [unsplash](), [visual-design-resources](), [visual-ui-design](), [web-design-resources]() ### AI overview A guide to 25 sources of free photography, artwork, illustrations, historical imagery, textures, and other visual assets for commercial design projects, with attention to whether assets are human-made and how licensing applies. ### Source excerpt AI images are taking over stock libraries, but the human-made web isn't dead yet. We found 25 places where designers can still find incredible free, real photography, art, and imagery for commercial use. ## Making thumbnails fast DevFeed: [Making thumbnails fast]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-09-14T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Image](), [pixel](), [Server](), [Software]() Tags: [article](), [blog](), [code](), [color](), [components](), [design](), [engineering](), [image](), [images](), [news](), [rgb](), [software](), [web-frontend]() ### AI overview Khan Academy explains how it generated custom video thumbnails by resizing, desaturating, lightening, color-tinting, and overlaying text and branding on source images. The post also explains pixel multiplication and why the team implemented the image operations themselves because its server infrastructure could not use available libraries. ### Source excerpt Note: This post contains quite a bit of LaTeX notation, which is not supported right now on this ... Read more ## Building a DIY handheld thermal camera for work and play DevFeed: [Building a DIY handheld thermal camera for work and play]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-03T18:19:43Z Content type: tutorial Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [data](), [Image](), [3D](), [User interface design]() Tags: [3d-printed](), [arduino](), [dfrobot](), [display](), [diy](), [diy-thermal-camera](), [i2c](), [range](), [thermal-camera](), [thermal-imaging](), [touchscreen](), [uno-r4]() ### AI overview This article describes a DIY handheld thermal camera built with an Arduino UNO R4 Minima, a Seeed Studio MLX90640 thermal imaging sensor, and a DFRobot touchscreen. The board reads the sensor's 32x24 infrared data over I2C, processes it into an image, and displays it through a user interface in a 3D-printed enclosure. ### Source excerpt Thermal cameras are really cool, because they give you the ability to clearly see something we humans usually can't: temperature. It is interesting to look at the world through that lens. Thermal cameras are also very useful when working with anything that involves heat, such as for spotting a leak in an HVAC system or [...] The post Building a DIY handheld thermal camera for work and play appeared first on Arduino Blog. ## How Yandex combined image and document text signals for image-search ranking DevFeed: [How Yandex combined image and document text signals for image-search ranking]() Original publisher: [Read original article]() Author: nikolaevkona (Яндекс) Published: 2026-08-10T08:00:19Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [яндекс](), [vlm](), [Image](), [realtime]() Tags: [image](), [machine-learning](), [ml](), [realtime](), [tag-4004cf5948d3](), [tag-8be2dbf54d97](), [vlm](), [yandex-e983188bc433]() ### AI overview Yandex describes using multimodal vision-language models to jointly assess an image and its associated document text for image-search ranking. The team distilled a larger model into lighter models for different pipeline stages and reports deployment in real-time search. ### Source excerpt Исторически в Яндекс Картинках релевантность документа оценивалась по двум сигналам: насколько запросу подходит само изображение и насколько -- текст, связанный с этим изображением. Такой подход позволяет учесть контент картинки и не провалиться на визуально трудноотличимых объектах, однако он же порождает проблему: в "серой зоне", когда текстовая релевантность не сонаправлена с картиночной, становится неочевидно, как именно агрегировать сигналы в финальный скор релевантности. Привет! Я Константин Николаев, занимаюсь внедрением нейротехнологий в Поиске по картинкам. В этой статье я расскажу, как наша команда научила модели смотреть на картинку и читать текст документа одновременно: начали с тяжёлой мультимодальной VLM ради максимального качества, а затем дистиллировали её в набор лёгких моделей -- по одной под каждую стадию пайплайна. Что из этого удалось довести до realtime-поиска с десятками тысяч запросов в секунду и как совместный анализ двух модальностей добавил 5% релевантных картинок в топ выдачи -- под катом. Читать далее ## Seasons time-lapse - the video DevFeed: [Seasons time-lapse - the video]() Original publisher: [Read original article]() Author: Nicolas Fränkel Published: 2026-06-07T00:00:00Z Content type: tutorial Language: en Sources: [Nicolas Fränkel]() Topics: [Image](), [Code](), [DateTime]() Tags: [art](), [cameras](), [code](), [development](), [images](), [information](), [location](), [metrics](), [model](), [pipeline](), [pixel](), [project](), [python](), [rotation](), [screen](), [speed](), [time-lapse](), [video]() ### AI overview The third and final post in a series explains how the author creates a time-lapse video from photographs taken from the same position over multiple years. It discusses ordering images by day of year and time of day, using EXIF metadata, and practical observations about video generation. ### Source excerpt In the first post of this series, I focused on the project foundations: what should I do to create a video from photos taken from the same position year after year? I dedicated the second part to aligning images. It wasn't as easy as I expected. I stumbled upon new concepts, such as ORB and RANSAC. In this third and final post, I want to tackle the video creation itself, explain some 'artistic' decisions, and leave the door open to future work. ## How we improved image download sizes on Medium with just four characters DevFeed: [How we improved image download sizes on Medium with just four characters]() Original publisher: [Read original article]() Author: Scott Batson Published: 2026-05-18T15:29:29Z Content type: article Language: en Sources: [Medium]() Topics: [Web Development](), [HTML](), [Image](), [JavaScript](), [Server-side rendering](), [Angular](), [Ember](), [Single-page application (SPA)]() Tags: [angular](), [html](), [image](), [javascript](), [programming](), [render](), [server-side-rendering](), [technology](), [time](), [web-development]() ### AI overview The article explains how modern HTML image features, including and srcset, can select images based on browser capabilities, screen dimensions, file types, layout, and color-scheme preferences. It contrasts these features with older JavaScript-based approaches to serving optimized images. ### Source excerpt HTML images just got better, with the subtlest change to your codebasePhoto by Adi Suyatno I know what you're thinking. Scott, I landed on Medium on a small but not mobile-small display and the download chunk related to images in my home feed was like 7% smaller. First of all, thank you for noticing. Second, what if I told you that you too can improve your total asset download size by adding just four characters to your code base? Sounds too good to be true, right? Well, I'm here to tell you, it's true... after I explain how we got here. The history of images on the web As a web developer, you've probably spent a lot of time thinking about and fighting with images. According to the HTTP Archive, even in today's modern landscape of "JavaScript everywhere" the majority of most website's transfer size is images. We've moved away from the "hero image on every page" ethos, but still we're an image-heavy internet. When I was getting my start in web development in 2010ish, we were still adapting to a "mobile-friendly" world. The majority of the web was built on WordPress. We didn't have a way back then to dynamically load an optimized image. 3G connections were the primary speed in the US for viewing the internet, so the practice was images "need to work on mobile" regardless of the user's device. For those of us writing Angular and Ember (because React wasn't a thing yet), you had to legitimately render the correct src based on context you could get from JavaScript. {{#if browserSmal}} {{else}} {{/if}} This is back when we wrote handlebars for our markup and the web was filling up with SPAs with no server-side-rendering. We did it this way because HTML lacked a way for specifying how we wanted to optimize images. Then in 2012, RICG introduced 2 new specs for image optimization, with 2 different use cases: elements and srcset. Picture Elements I love the element. It gives us a lot of control ## Marc Andreessen Egg Game DevFeed: [Marc Andreessen Egg Game]() Original publisher: [Read original article]() Author: Nolen Royalty (eieiogames@gmail.com) Published: 2026-05-07T00:00:00Z Content type: article Language: en Sources: [eieio.games]() Topics: [Image](), [object]() Tags: [game](), [images](), [lighting]() ### AI overview A playful developer article about Marc Andreessen Egg Game, a game for drawing eggs that resemble Marc Andreessen. It describes the game's manifesto and explains technical challenges including egg lighting with a normal map and scoring its speedrun mode. ### Source excerpt Marc Andreessen Egg Game helps you make eggs that look like Marc Andreessen. ## Chainguard EKS add-ons are now available in the AWS Marketplace DevFeed: [Chainguard EKS add-ons are now available in the AWS Marketplace]() Original publisher: [Read original article]() Published: 2026-04-30T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [Amazon EKS](), [aws-marketplace](), [chainguard](), [Security](), [supply-chain-security](), [Image](), [Containers]() Tags: [aws-eks-add-ons](), [aws-marketplace](), [chainguard](), [chainguard-aws](), [chainguard-containers](), [chainguard-eks-add-ons](), [compliance](), [eks-add-ons](), [fips](), [fips-140-3](), [kubernetes](), [security](), [software-supply-chain]() ### AI overview Chainguard announces that its EKS add-ons are available in AWS Marketplace. The five add-ons provide networking, DNS, storage, and traffic-management components, with zero-known-CVE images and FIPS 140-3 validation for organizations that need greater control over security and compliance. ### Source excerpt Chainguard delivers zero-CVE, FIPS 140-3 EKS add-ons on AWS Marketplace, simplifying secure Kubernetes for regulated environments with full control. ## OpenAI and NORAD team up to bring new magic to "NORAD Tracks Santa" DevFeed: [OpenAI and NORAD team up to bring new magic to "NORAD Tracks Santa"]() Original publisher: [Read original article]() Published: 2025-12-01T06:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [OpenAI](), [Image](), [Website]() Tags: [art](), [chatgpt](), [company](), [openai](), [storytelling](), [tools]() ### AI overview OpenAI and NORAD collaborated on three holiday tools within ChatGPT for the NORAD Tracks Santa program: an elf photo transformation, a toy-idea coloring-page generator, and a customizable Christmas story creator. ### Source excerpt OpenAI and NORAD are bringing new magic to "NORAD Tracks Santa" with three ChatGPT holiday tools that let families create festive elves, toy coloring pages, and custom Christmas stories. ## A Cornucopia of Updates Make Discord on Desktop Fresher Than a Crisp Fall Breeze DevFeed: [A Cornucopia of Updates Make Discord on Desktop Fresher Than a Crisp Fall Breeze]() Original publisher: [Read original article]() Author: Clyde Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [Discord Blog]() Topics: [Discord](), [Emoji](), [Image](), [servers]() Tags: [app](), [desktop](), [discord](), [features](), [gif](), [image](), [improvements](), [server](), [servers](), [updates]() ### AI overview Discord's desktop updates include faster emoji creation with in-chat cropping, rotation, previews, naming, and server selection. The article also highlights a redesigned Settings page, easier Group DM customization, expanded game integrations, and Family Center improvements. ### Source excerpt This fall, emoji making gets faster, the Settings page gets a redesign, Group DMs are easier to customize, more games gain support for special Discord-powered capabilities, and Family Center gets some expanded features. Take a peek at what's new this season! ## StreetReaderAI: Towards making street view accessible via context-aware multimodal AI DevFeed: [StreetReaderAI: Towards making street view accessible via context-aware multimodal AI]() Original publisher: [Read original article]() Published: 2025-10-29T16:38:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Accessibility](), [multimodal-ai](), [Artificial Intelligence](), [Google](), [Image](), [alt text](), [real-time]() Tags: [accessibility](), [ai](), [alt-text](), [audio](), [gaming](), [generative-ai](), [human-computer-interaction-and-visualization](), [machine-intelligence](), [multimodal](), [multimodal-ai](), [natural-language-processing](), [navigation](), [real-time](), [research](), [screen](), [vision](), [voice]() ### AI overview Google Research introduces StreetReaderAI, a proof-of-concept accessible street view prototype that uses context-aware, real-time multimodal AI to interpret streetscape imagery. Designed with blind and sighted accessibility researchers, it provides audio-led exploration, keyboard and voice navigation, directional feedback, movement guidance, and descriptions of nearby places and geographic information. ### Source excerpt Generative AI ## A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums DevFeed: [A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums]() Original publisher: [Read original article]() Published: 2025-10-20T21:54:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Generative AI](), [Synthetic Data Generation](), [synthetic-data](), [Image](), [Google](), [datasets](), [Machine learning]() Tags: [2025](), [ai](), [ai-models](), [data](), [datasets](), [fine-tuning](), [generation](), [generative](), [generative-ai](), [google](), [image](), [images](), [machine-intelligence](), [machine-learning](), [privacy](), [security-privacy-and-abuse-prevention](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [training]() ### AI overview Google Research introduces a method for generating differentially private synthetic photo albums. The approach translates image data into an intermediate text representation and generates albums hierarchically to preserve thematic coherence and character consistency across multiple photos. It uses differentially private fine-tuning, such as DP-SGD, to produce representative synthetic data without unique details from individual users. ### Source excerpt Generative AI ## DigitalOcean Gradient AI Platform adds image generation, knowledge-base auto-indexing, VPC security, and developer tools DevFeed: [DigitalOcean Gradient AI Platform adds image generation, knowledge-base auto-indexing, VPC security, and developer tools]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2025-10-02T07:12:50Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [AI Platform](), [Artificial Intelligence](), [Digital Ocean](), [text-to-image](), [API](), [Serverless](), [stable-diffusion](), [multimodal](), [Security](), [Image](), [OpenAI]() Tags: [2025](), [accelerate](), [ai](), [ai-platform](), [ai-tools](), [api](), [api-integration](), [authentication](), [code](), [data](), [development](), [generation](), [github](), [image](), [openai](), [platform](), [product-updates](), [range](), [text-to-image](), [vpc](), [workflow]() ### AI overview DigitalOcean announces new Gradient AI Platform capabilities, including programmatic image generation through the Serverless Inference API, Knowledge Base Auto-Indexing, security controls, and additional developer tools for building AI applications. ### Source excerpt At Deploy London 2025, we shared the next chapter of the Gradient AI Platform. We're making it easier for developers and businesses to build production-ready AI applications, whether you're experimenting with your first agent or scaling an enterprise workload. Today, in that spirit, we're introducing a new wave of features that expand what you can build with the Gradient AI Platform, give you greater security and control, and accelerate your development workflow. Expanding capabilities for Gradient AI Platform AI applications are becoming more multimodal and data-driven, able to work with text, images, audio, and other formats. With Image Model Support and Knowledge Base Auto-Indexing, these new Gradient AI Platform features make it easier than ever to give your agents a wide range of inputs and knowledge sources. Image Model Support You can now generate images programmatically using text prompts through Gradient AI Platform's Serverless Inference API, powered by OpenAI's gpt-image-1 model. This is the platform's first non-text modality (with more coming soon), expanding our capabilities from text-only to include text-to-image generation. Text-to-image generation - Generate images directly via the Serverless Inference API. API integration - OpenAI compatible using the image generation endpoint. For code examples and setup instructions, check out the DigitalOcean Gradient AI starter kit on GitHub. Unified billing - Image generation charges appear on your DigitalOcean account alongside other services. Developer-first workflow - Direct API access without requiring UI-based tools or self-hosting. Enterprise authentication - Use Model Access Keys for secure access. With this capability, you can create images using natural language prompts for projects such as content generation, marketing assets, product imagery, and more. Get started with Image Model Support -> Knowledge Base Auto-Indexing Keep your agents up to date without the manual work. With auto-indexing, new and u ## Apple Photos App Corrupts Images DevFeed: [Apple Photos App Corrupts Images]() Original publisher: [Read original article]() Published: 2025-09-16T23:59:23Z Content type: article Language: en Sources: [Aaron Patterson]() Topics: [App](), [file](), [import](), [debug](), [Image](), [Hardware](), [USB]() Tags: [app](), [camera](), [debug](), [file](), [files](), [hardware](), [images](), [import](), [photo](), [photos](), [trust](), [usb]() ### AI overview The article describes intermittent image corruption in Apple Photos when importing photos from an OM System OM-1 camera. The author reports corrupted JPEG and RAW files, estimates losing about 30% of the images from one event, investigates possible hardware causes, and changes to a different import workflow after losing trust in Photos. ### Source excerpt The Apple Photos app sometimes corrupts images when importing from my camera. I just wanted to make a blog post about it in case anyone else runs into the problem. I've seen other references to this online, but most of the people gave up trying to fix it, and none of them went as far as I did to debug the issue. I'll try to describe the problem, and the things I've tried to do to fix it. But also note that I've (sort of) given up on the Photos app too. Since I can't trust it to import photos from my camera, I switched to a different workflow. Here is a screenshot of a corrupted image in the Photos app: How I used to import images I've got an OM System OM-1 camera. I used to shoot in RAW + jpg, then when I would import to Photos app, I would check the "delete photos after import" checkbox in order to empty the SD card. Turns out "delete after import" was a huge mistake. Getting corrupted images I'm pretty sure I'd been getting corrupted images for a while, but it would only be 1 or 2 images out of thousands, so I thought nothing of it (it was probably my fault anyway, right?) But the problem really got me upset when last year I went to a family member's wedding and took tons of photos. Apple Photos combines RAW + jpg photos so you don't have a bunch of duplicates, and when you view the images in the photos app, it just shows you the jpg version by default. After I imported all of the wedding photos I noticed some of them were corrupted. Upon closer inspection, I found that it sometimes had corrupted the jpg, sometimes corrupted the RAW file, and sometimes both. Since I had been checking the "delete after import" box, I didn't know if the images on the SD card were corrupted before importing or not. After all, the files had been deleted so there was no way to check. I estimate I completely lost about 30% of the images I took that day. Losing so many photos really rattled me, but I wanted to figure out the problem so I didn't lose images in the future. Narrowing down t ## New Firebase AI Logic features to explore - September 2025 updates DevFeed: [New Firebase AI Logic features to explore - September 2025 updates]() Original publisher: [Read original article]() Author: Miguel Ramos; Rachel Saunders Published: 2025-09-10T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [SDKs](), [On-device AI](), [Image](), [API](), [Web](), [Android](), [Flutter](), [Unity](), [Chrome]() Tags: [ai](), [ai-logic](), [android](), [api](), [app-check](), [debug](), [firebase](), [firebase-ai-logic](), [flutter](), [gemini](), [hybrid-inference](), [image](), [images](), [inference](), [js](), [news](), [observability](), [offline](), [on-device](), [on-device-ai](), [unity](), [updates]() ### AI overview Firebase AI Logic's September 2025 updates add Gemini Live API voice interactions, client-side image editing and generation with Imagen, Nano Banana image workflows, hybrid on-device inference for web apps, and additional monitoring and observability capabilities. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Reducing Infrastructure Complexity: Why I Ditched S3+CloudFront+Lambda DevFeed: [Reducing Infrastructure Complexity: Why I Ditched S3+CloudFront+Lambda]() Original publisher: [Read original article]() Author: Shivam Anand Published: 2025-08-24T07:48:09Z Content type: opinion Language: en Sources: [Shivam Anand - DevOps & Cloud Engineering Blog]() Topics: [Amazon S3](), [migration](), [Optimization](), [Image](), [Cross-origin resource sharing (CORS)]() Tags: [api-keys](), [aws](), [aws-s3](), [blog-performance](), [cloud-infrastructure](), [cloudfront](), [cloudinary](), [cors](), [devops](), [devops-cloud-infrastructure](), [iam](), [image](), [image-transformations](), [lambda](), [lambda-edge](), [media-optimization](), [migration](), [optimization](), [performance](), [strapi]() ### AI overview The author describes migrating a blog's media management from AWS S3, CloudFront, and Lambda to Cloudinary after finding the multi-service setup unnecessarily complex for a small blog. They highlight Cloudinary's simpler onboarding and URL-based image transformations. ### Source excerpt Discover how I simplified media management for my blog by moving from AWS S3, CloudFront, and Lambda to Cloudinary. Learn about URL-based transformations, automatic optimization, and faster performance. ## Generate Images with Claude and Hugging Face DevFeed: [Generate Images with Claude and Hugging Face]() Original publisher: [Read original article]() Author: shaun smith Published: 2025-08-19T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Claude](), [hugging face](), [Model Context Protocol](), [MCP Server](), [Artificial Intelligence](), [gradio](), [flux](), [Image](), [qwen](), [spaces]() Tags: [anthropic](), [applications](), [article](), [artificial-intelligence](), [blog](), [claude](), [flux](), [generation](), [gradio](), [how-to](), [hugging-face](), [image](), [images](), [llm](), [mcp](), [mcp-server](), [models](), [qwen](), [spaces](), [text-to-image](), [tools]() ### AI overview This tutorial explains how to connect Claude to Hugging Face Spaces through the Hugging Face MCP Server to generate detailed images with AI models. It highlights prompt assistance, visual iteration, model selection, and examples including FLUX.1 Krea [dev] and Qwen-Image. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## EU chat-control proposal would require messaging services to scan photos and links with AI DevFeed: [EU chat-control proposal would require messaging services to scan photos and links with AI]() Original publisher: [Read original article]() Published: 2025-08-17T15:15:00Z Content type: opinion Language: fr Sources: [Bert Hubert's writings]() Topics: [Meta](), [Image]() Tags: [2025](), [exploitation](), [images](), [meta](), [photos](), [surveillance]() ### AI overview The article discusses an EU chat-control proposal, reportedly driven by Denmark and supported by numerous member states, that would require messaging services such as WhatsApp and Signal to use AI to inspect users' photos and links. It says suspected material and associated details could be reported to Europol and local police, while refusal to allow scanning could restrict image and link sharing. The article also notes that Signal has said it would leave the EU if the law were adopted. ### Source excerpt En bref, sous l'impulsion du Danemark, de nombreux États membres de l'UE demandent que les messageries comme WhatsApp/Signal/etc. soient contraintes d'inspecter toutes nos photos et tous nos liens en utilisant de l'IA. Si l'IA a le moindre "doute" quant à la possibilité qu'il s'agisse de pédopornographie, votre photo, votre localisation, votre numéro de téléphone ainsi que d'autres détails sont signalés à Europol et aux forces de police locales. La suite n'est pas précisée, mais vos vacances à Chypre pourraient se vite se terminer par l'obligation d'expliquer vos photos au poste de police local. ## How to Generate New Art From Existing Images Using Deep Learning? DevFeed: [How to Generate New Art From Existing Images Using Deep Learning?]() Original publisher: [Read original article]() Author: Damilare Keshinro Published: 2025-07-24T04:56:41Z Content type: tutorial Language: en Sources: [Baeldung - CS]() Topics: [Deep neural networks](), [Tensorflow](), [Tutorial](), [Image](), [Computer science]() Tags: [building](), [computer-science](), [computer-vision](), [computer-vision-image-processing](), [deep-learning](), [generate](), [how-to](), [image-processing](), [images](), [model](), [techniques](), [tensorflow]() ### AI overview A tutorial on neural style transfer using TensorFlow and a pretrained VGG16 model. It explains how to combine the content of an owl image with the artistic style of Edvard Munch's "Scream" by extracting features, defining losses, preprocessing images, and optimizing the result. ### Source excerpt A walkthrough of building an art style transfer program using deep learning techniques and tools like TensorFlow and the VGG16 model. The post How to Generate New Art From Existing Images Using Deep Learning? first appeared on Baeldung on Computer Science. ## Easy way to upload, transform and deliver files and images (Sponsored) DevFeed: [Easy way to upload, transform and deliver files and images (Sponsored)]() Original publisher: [Read original article]() Author: David Walsh Published: 2025-03-24T22:58:30Z Content type: article Language: en Sources: [David Walsh]() Topics: [file](), [Image](), [Developer experience](), [Back end](), [Python]() Tags: [ascii](), [back-end](), [conversion](), [documentation](), [files](), [gif](), [images](), [python](), [qr-code](), [sponsored](), [uncategorized]() ### AI overview This sponsored developer article presents Filestack as a service for uploading, transforming, and delivering files and images. It describes a file-uploading interface, integrations with services such as Google Drive, Dropbox, GitHub, Facebook, and Instagram, a Python library for back-end uploads, documentation, and transformations for images, videos, and documents. ### Source excerpt Managing media is a really difficult task if you try to do all of it yourself, especially if the media comes from other sources. The file can be submitted in any state and size, but what if you need something really specific? You can code it all yourself or you can use an awesome service [...] The post Easy way to upload, transform and deliver files and images (Sponsored) appeared first on David Walsh Blog. ## Image replacement in Canva designs using reverse image search DevFeed: [Image replacement in Canva designs using reverse image search]() Original publisher: [Read original article]() Author: Sam Jacobs Published: 2025-01-28T09:05:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [Machine learning](), [Image](), [Requirements](), [Database](), [recommendation systems]() Tags: [backend](), [database](), [design](), [embedding](), [frontend](), [image](), [intellectual-property](), [machine-learning](), [models](), [requirements](), [search]() ### AI overview Canva describes comparing image embedding models to build a scalable reverse image search system for replacing images in designs. The proposed system considers subject, color, tone, positioning, background, emotion, and aspect ratio while supporting IP-safe search across more than 150 million images. ### Source excerpt Qualitative comparison of image embedding models to power a scalable similar-image replacement system for Canva designs. ## Fix your website's Largest Contentful Paint by optimizing image loading DevFeed: [Fix your website's Largest Contentful Paint by optimizing image loading]() Original publisher: [Read original article]() Author: debugbear Published: 2025-01-13T00:00:00Z Content type: tutorial Language: en Sources: [MDN Blog]() Topics: [Core Web Vitals](), [Website](), [Image](), [Optimization](), [HTML](), [Performance Testing](), [real user monitoring](), [Network](), [browser](), [JavaScript](), [CSS]() Tags: [article](), [browser](), [core-web-vitals](), [css](), [how-to](), [html](), [javascript](), [network](), [optimization](), [performance](), [performance-testing](), [user-experience](), [web]() ### AI overview This tutorial explains how image loading affects Largest Contentful Paint and Core Web Vitals. It covers image discovery through HTML, CSS, and JavaScript, request chains, network waterfalls, browser image-processing stages, and techniques for diagnosing and improving loading performance. ### Source excerpt Learn techniques to improve the Largest Contentful Paint metric, a part of Core Web Vitals, for your website. ## Computer-aided diagnosis for lung cancer screening DevFeed: [Computer-aided diagnosis for lung cancer screening]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-20T20:54:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Machine learning](), [Google](), [Artificial Intelligence](), [Image]() Tags: [false-positives](), [google](), [health](), [healthcare](), [japan](), [machine-learning](), [reporting](), [user-experience]() ### AI overview Google Research describes a machine-learning system for lung cancer screening that analyzes CT scans, assigns one of four cancer-suspicion ratings, and highlights corresponding regions of interest. The system was evaluated in randomized reader studies in the US and Japan using local scoring systems and realistic image viewers. ### Source excerpt Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier. In the US, screening involves annual scans, with some countries or cases recommending more or less frequent scans. The United States Preventive Services Task Force recently expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups. However, false positives (i.e., incorrectly reporting a potential cancer in a cancer-free patient) can cause anxiety and lead to unnecessary procedures for patients while increasing costs for the healthcare system. Moreover, efficiency in screening a large number of individuals can be challenging depending on healthcare infrastructure and radiologist availability. At Google we have previously developed machine learning (ML) models for lung cancer detection, and have evaluated their ability to automatically detect and classify regions that show signs of potential cancer. Performance has been shown to be comparable to that of specialists in detecting possible cancer. While they have achieved high performance, effectively communicating findings in realistic environments is necessary to realize their full potential. To that end, in "Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the US and Japan", published in Radiology AI, we investigate how ML models can effectively communicate findings to radiologists. We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening. The ## Testing Images in Blog Posts DevFeed: [Testing Images in Blog Posts]() Original publisher: [Read original article]() Published: 2024-03-16T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Testing](), [Image](), [test]() Tags: [blog](), [blog-posts](), [images](), [posts](), [testing]() ### AI overview A blog post demonstrates testing different types and sizes of images, including remote images loaded from URLs and local images stored in a directory. ### Source excerpt Testing Different Types of Images Remote Image Here's a remote image from a URL: . [Remote test image](https://picsum... 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