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 unin-tended intrinsic noise amplification are still issues that remain unsolved. This paper proposes a zero-reference end-to-end two-stage 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.