Multi-Feature Learning for Low-Light Image Enhancement

Low-light images are not suitable for direct use in computer vision tasks due to the low visibility of the images. The existing low-light image enhancement methods usually produce colour distortion and noise amplification. This paper proposes a low-light image enhancement method based on multi-feature learning. Our method is different from most methods that decompose the image into two parts: an illumination image and a reflection image. In our learning model, these features are designed based on the pixel level, which makes the model concise and ensures colour fidelity. Our network learns three categories of image features: global features, local features, and texture features. A loss function part based on SSIM is used to ensure that multiple features are extracted effectively. Furthermore, a loss function part based on Sobel is designed to suppress noise and protect the image details. Subjective and objective experimental results demonstrate the effectiveness of our approach for low-light image enhancement.