Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining

Abstract

Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillance systems and intelligent vehicles. In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attention module with a fusion network. Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales. Furthermore, feature maps are integrated by channel attention. Our proposed network learns confidence maps of four sub-band images derived from the wavelet transform of the original images. Finally, the clear image can be well restored via the wavelet reconstruction and fusion of the low-frequency part and high-frequency parts. Several experimental results on synthetic and real images present that the proposed algorithm outperforms state-of-the-art methods.

Accepted to IEEE ICIP 2020

References in corpus (2)

• Recurrent Models of Visual Attention
• Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement

Cited by in corpus (6)

• LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement
• WaveNets: Wavelet Channel Attention Networks
• ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation
• S3Net: A Single Stream Structure for Depth Guided Image Relighting
• DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
• Multi-modal Bifurcated Network for Depth Guided Image Relighting