Abstract
Low-light image enhancement has been a challenging topic in computer vision. In order to recover colors and detailed textures in images, several data-driven enhancement based methods have been developed and obtained encouraging results. However, the network generalization ability is not satisfactory due to the uncertainty of the collected data. In order to address this issue, we propose a network with texture enhancement (TEENet), which synergizes the image brightness recovery and recovers the texture information lost during the process. To recover image brightness, we propose a low-light image enhancement network with a squeeze-excitation operation and construct a texture-optimized network that combines contextual aggregation information to recover texture loss during the enhancement process. We conducted network performance tests using different data and ablation experiments to verify the performance of different components.
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