Zero-LEINR: Zero-Reference Low-light Image Enhancement with Intrinsic Noise Reduction

Zero-reference deep learning-based methods for low-light image enhancement sufficiently mitigate the difficulty of paired data collection while keeping the great generalization on various lighting conditions. However, color bias and unintended intrinsic noise amplification are still issues that remain unsolved. This paper proposes a zero-reference end-to-end twostage network (Zero-LEINR) for low-light image enhancement with intrinsic noise reduction. In the first stage, we introduce a Color Preservation and Light Enhancement Block (CPLEB) that consists of a dual branch structure with different constraints to correct the brightness and preserve the correct color tone. In the second stage, Enhanced-Noise Reduction Block (ENRB) is applied to remove the intrinsic noises being enhanced during the first stage. Due to the zero-reference two-stage structure, our method is generalized to enhance low-light images with correct color tone on unseen datasets and reduce the intrinsic noise simultaneously.