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The performance of vision based applications is often limited by low-light imaging environments. While various methods have been proposed to enhance image contrast, noise is inevitably amplified. In addition, most methods result in overenhancement in bright regions. In this paper, we consider a convolutional neural network, named as LERANet, to decouple a dark image into reflectance and illumination, which can thus enhance contrast and reduce noise. An attention module is also integrated in the network to avoid over-enhancement. Experimental results demonstrate the effectiveness of the proposed LERANet on noise suppression and detail preservation. In addition, both subjective and objective comparisons with state-of-the-art algorithms indicate the superiority of the proposed method.

LERANet: Low-light Enhancement Network based on Retinex and Attention Renjie He, Xintao Guo, Wei Zhou, and Mingyi He