SWAM-Net $$+$$ : Selective Wavelet Attentive M-Network $$+$$ for Single Image Dehazing

Abstract

Image dehazing is an ill-posed issue in low-level computer vision; therefore, it grabbed many researchers’ attention. The key mechanism to improve dehazing performance remains unclear, although many existing network pipelines work fine. To improve the performance of the image dehazing network, a hierarchical model named “Selective Attentive Wavelet M-Net+” (SWAM-Net+) was proposed. In order to enrich the features from the wavelet domain, a “Selective Wavelet Attentive Module” was introduced in M-Net+. Several key components of our network are used for extracting the multiscale features through parallel multi-resolution convolution channels. Contextual information is collected using a dual attention unit, and the attention is based on multiscale feature aggregation. We replaced summation and concatenation operations by introducing the Selective Kernel Feature Fusing module to achieve feature aggregation. Furthermore, our network achieves comprehensively better performance results on the RESIDE dataset both qualitatively and quantitatively.