Joint Enhancement and Denoising Method via Sequential Decomposition
This addresses the issue of noise amplification and detail loss in low-light image processing for applications like photography and computer vision, though it appears incremental as it builds on existing Retinex-based approaches.
The paper tackles the problem of low-light image enhancement where existing methods often amplify noise or lose details, by proposing a joint enhancement and denoising strategy that uses sequential Retinex decomposition to estimate smoothed illumination and noise-suppressed reflectance, achieving better or comparable quality to state-of-the-art methods.
Many low-light enhancement methods ignore intensive noise in original images. As a result, they often simultaneously enhance the noise as well. Furthermore, extra denoising procedures adopted by most methods ruin the details. In this paper, we introduce a joint low-light enhancement and denoising strategy, aimed at obtaining well-enhanced low-light images while getting rid of the inherent noise issue simultaneously. The proposed method performs Retinex model based decomposition in a successive sequence, which sequentially estimates a piece-wise smoothed illumination and a noise-suppressed reflectance. After getting the illumination and reflectance map, we adjust the illumination layer and generate our enhancement result. In this noise-suppressed sequential decomposition process we enforce the spatial smoothness on each component and skillfully make use of weight matrices to suppress the noise and improve the contrast. Results of extensive experiments demonstrate the effectiveness and practicability of our method. It performs well for a wide variety of images, and achieves better or comparable quality compared with the state-of-the-art methods.