Decreased visibility, intensive noise, and biased color are the common
problems existing in low-light images. These visual disturbances further reduce
the performance of high-level vision tasks, such as object detection, and
tracking. To address this issue, some image enhancement methods have been
proposed to increase the image contrast. However, most of them are implemented
only in the spatial domain, which can be severely influenced by noise signals
while enhancing. Hence, in this work, we propose a novel residual recurrent
multi-wavelet convolutional neural network R2-MWCNN learned in the frequency
domain that can simultaneously increase the image contrast and reduce noise
signals well. This end-to-end trainable network utilizes a multi-level discrete
wavelet transform to divide input feature maps into distinct frequencies,
resulting in a better denoise impact. A channel-wise loss function is proposed
to correct the color distortion for more realistic results. Extensive
experiments demonstrate that our proposed R2-MWCNN outperforms the
state-of-the-art methods quantitively and qualitatively.

提出一种新颖的残差循环多小波卷积神经网络 R2-MWCNN 用于低光图像增强和降噪，该网络通过多层离散小波变换将输入特征图划分为不同的频率，提出了通道损失函数来更正颜色扭曲，实验证明该方法在量化和定性上优于现有的方法。