Contrast-Aware Image Enhancement Network for Low-Light Images

Low-light image enhancement is a challenging problem in computer vision due the inherently fragile nature of low-light images, which suffer from low brightness, poor contrast, and color distortion. This study proposes a contrast-aware low-light image enhancement network in the wavelet domain. The contrast measure defined as the ratio of the high-frequency coefficient to the low-frequency coefficient serves as a key factor in restoring natural illumination. The proposed network includes U-shaped lightening and sharpening blocks, complemented by lightened and sharpened feature attention blocks. The lightening block, which uses wavelet low-frequency subbands as input, is employed to restore brightness. The lightened information is then transmitted to high-frequency subbands through the lightened feature attention block. The sharpening block is used to enhance image detail within high-frequency subbands. The sharpened information is then relayed to the lightened low-frequency subbands to balance the image contrast. The proposed network achieves balanced contrast by facilitating the exchange of enhancement information between the low- and high-frequency subbands. Additionally, we introduce a saturation guide block to restore image color effectively. This study restores each component through wavelet decomposition and uses the concept of contrast to restore it to a natural image. This could be further developed by combining color space decomposition. The effectiveness of the proposed network is evaluated through extensive experiments on well-known benchmarks. Simulation results confirm that the proposed low-light enhancement network surpasses other state-of-the-art approaches.