Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement
This work addresses the practical issue of robust low-light image enhancement for real-world applications by handling noise variations in sRGB space, which is incremental as it builds on model-based denoising methods.
The paper tackled the problem of enhancing low-light images in sRGB color space by addressing both poor visibility and varying noise levels, proposing an adaptive unfolding total variation network (UTVNet) that learns noise level maps to recover details and suppress noise, with experiments showing superior performance over state-of-the-art methods.
Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation becomes more complicated due to the effect of the image processing pipeline. Nevertheless, most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level, leading them impractical due to the lack of robustness. To address this issue,we propose an adaptive unfolding total variation network (UTVNet), which approximates the noise level from the real sRGB low-light image by learning the balancing parameter in the model-based denoising method with total variation regularization. Meanwhile, we learn the noise level map by unrolling the corresponding minimization process for providing the inferences of smoothness and fidelity constraints. Guided by the noise level map, our UTVNet can recover finer details and is more capable to suppress noise in real captured low-light scenes. Extensive experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods.