A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation

Abstract

A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.