The rapid growth of the e-commerce fashion industry has increased the challenge of accurately representing clothing colors in online product images, often causing differences between displayed images and actual products that reduce customer confidence and satisfaction. COLORIQ addresses this issue through an AI-powered color correction system designed specifically for shoppers to provide realistic and accurate clothing color representation before purchase. The system utilizes a custom U-Net deep learning architecture integrated with VGG16-based perceptual learning and implemented using PyTorch to enhance color fidelity while preserving image quality and structural consistency. Developed as a full-stack web platform using Next.js, React, FastAPI, PostgreSQL, JWT authentication, and Azure Blob Storage, COLORIQ offers real-time image processing, image comparison, and personalized user features. By bridging the gap between digital imagery and physical product appearance, the platform enables shoppers to make more informed purchasing decisions and enhances trust and satisfaction in online fashion shopping.
Tools: Next.js,FastAPI,REACT,POSTGRESQL,Azure Blob Storage,PyTorch,TypeScript,VS CODE
Department: Department of Computer Science
Poster
