MFFE: Multi-scale Feature Fusion Enhanced Net for image dehazing

Abstract

It is very challenging to perform dehazing operations on single haze images taken due to information degradation, for which we propose a novel single-image dehazing network based on U-Net. Most existing image dehazing algorithms only focus on whether they can remove the haze, but ignore the quality of the final dehazed image, which can lead to problems such as loss of image detail information and texture blurring, affecting the final recovery. We use the enhancement and error feedback mechanisms inside the super-resolution algorithm to gradually recover the haze-free images by introducing the Strengthen-Operate-subtract strategy in the decoder and demonstrate their effectiveness for the dehazing problem. Meanwhile, to solve the problem of missing information and utilization in the traditional U-Net structure, we design a multiscale feature fusion module that can effectively compensate for the missing contextual information and make full use of the disjoint features. In addition, an improved local binary pattern and SE attention mechanism are used to help the network obtain clearer details and texture images, and the accuracy of the enhanced dehazing network is improved using residual learning. We analyze the effectiveness of the proposed algorithm and show through extensive evaluation that our proposed model can be effectively used for single image dehazing and also has good performance compared to state-of-the-art methods on the test dataset.