Building a Browser-Based AI Image Upscaler

Introduction In this article, we'll explore how to implement a powerful browser-based AI image upscaler that uses deep learning models to enlarge images while maintaining quality. This tool supports 2x and 4x upscaling using Real-ESRGAN and Real-CUGAN models, running entirely in the browser with WebGPU or WebGL acceleration. Why Browser-Based Upscaling? 1. Privacy Protection When users upscale images in the browser, their photos never leave their device. 2. Zero Server Costs Running the AI model in the browser eliminates the need for: GPU servers for deep learning inference Bandwidth for uploading/downloading images API costs for third-party upscaling services 3. Offline Capability Once models are cached, users can upscale images without an internet connection. Technical Architecture Core Implementation 1. Data Structures interface ImageItem { id : string ; file : File ; originalUrl : string ; processedUrl : string | null ; status : ' pending ' | ' processing ' | ' done ' | ' error ' ;