Zero-DCE xT: A Computational Approach to Addressing Low-Light Image Enhancement Challenges

In the dynamic field of digital image processing, effectively enhancing low-light images presents a significant challenge. This study narrows its focus to the preservation of natural aesthetics in low-light enhancement. The knowledge gap identified is the need for an approach that balances visibility improvement with authentic color retention without computational burden. We introduce the Zero-DCE xT model, an evolution of the Zero-DCE framework, optimized for structure and function in low-light conditions. Our experimental work shows the Zero-DCE xT model's capability to significantly reduce prediction errors and improve image quality, achieving high scores in PSNR, VIF, and SSIM across various datasets. Notably, Zero-DCE xT combines visual appeal with computational efficiency, making it ideal for real-time applications. The implications of our findings suggest that the Zero-DCE xT model sets a new standard for low-light image processing, marrying robust performance with economical computation, and paving the way for future research in advanced imaging systems.