Unsupervised Low-Light Image Enhancement via Feature Smoothing and Curve Regression

Various methods have been discussed in the field of Low-light Image Enhancement (LIE), however the inability to effectively restrain noise still led to unsupervised LIE remaining a challenge. Traditional supervised LIE methods had the advantage of suppressing noise, but they suffer from generalization problems since the pairing of low-light and normal-light images are always required in the process. To this end, this letter introduces an unsupervised LIE enhancement method that eliminates the need for paired images and effectively suppresses noise. The method utilizes the Retinex image decomposition technique to split the image into illumination and reflection components and processes the features of each part separately. In addition, we designed two deep learning architectures, lighting net (LINet) and denoising net (DNNet), to brighten the illumination component and denoise the reflection component respectively. We validate our method on two well-known open datasets and compared it with the state-of-the-art approaches. The experiments show that that the proposed method performs well in dimly lit scenes and visual verification, all quantitative metrics have at least 8% improvement on the best performing validation set.