Multiscale feature fusion deep network for single image dehazing with continuous memory mechanism

Abstract

Haze scenes seriously degrade the performance of computer vision systems. Thus, the single image dehazing based on convolutional neural network (CNN), as an important direction of computer vision, has always attracted extraordinary attentions of researchers and made great progress on dehazing effects. However, there are still some non-trivial issues such as uneven dehazing and color distortion, which are not well addressed. To alleviate these issues, this work proposes a new multiscale feature fusion image dehazing network incorporating a contiguous memory mechanism (MFFDN-CM). Specifically, the pixel attention mechanism, continuous memory strategy and residual dense blocks are jointly integrated into the dehazing model with a prevalent encoder-decoder structure(U-Net). Firstly, our model obtains multiscale feature maps by subsampling operations, and further employs skip connections between the corresponding network layers to connect the feature maps between the encoder and the decoder for good feature fusion. Then, we introduce a continuous memory residual block to strengthen the information flows for feature reuse. Moreover, to leverage detail representation and accomplish adaptive dehazing according to the haze density, MFFDN-CM adopts a pixel attention module on the skip connections to combine the residual dense block module of the corresponding decoding layers. Finally, multiple residual blocks are exploited on the bottleneck in encoder-decoder structure to prevent network performance degradation due to vanishing gradients. Experimental results demonstrate the proposed model can achieve better dehazing performance than the state-of-the-art methods based on other deep neural networks.