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Introduction DIP_Zero-DCE is a project focused on Zero-Reference Deep Image Prior (Zero-DCE) techniques for enhancing low-light and dark image quality. This method leverages deep learning to improve the visibility and clarity of images captured under low-light conditions without requiring reference images for training. DIP_Zero-DCE provides a state-of-the-art solution for various applications in image enhancement, particularly in challenging lighting environments.

Features Zero-Reference Enhancement: Enhances image quality without needing reference images, making it suitable for scenarios where no ground-truth data is available. Deep Learning Approach: Utilizes advanced deep learning techniques to improve the visibility and detail of low-light and dark images. Real-Time Processing: Designed for efficient processing, allowing for real-time or near-real-time image enhancement. Flexible Integration: Can be integrated into various applications and workflows for image enhancement, including mobile apps and desktop software. High-Quality Outputs: Produces high-quality enhanced images with improved brightness, contrast, and detail. Comprehensive Documentation: Includes detailed instructions and examples for setting up the enhancement model, processing images, and integrating the solution into different systems.