Rain Streak Removal for Single Image via Kernel Guided CNN

TL;DR

This paper introduces a kernel guided CNN that models rain streak motion blur to improve single image rain removal, achieving state-of-the-art results by integrating motion kernel estimation with deep learning.

Contribution

The novel framework jointly learns rain streak motion blur kernels and removes rain using a guided CNN, addressing limitations of previous methods that neglected motion blur effects.

Findings

Achieves superior rain removal performance on synthetic and real data.

Preserves image texture and contrast effectively.

Outperforms existing deep learning rain streak removal methods.

Abstract

Rain streak removal is an important issue and has recently been investigated extensively. Existing methods, especially the newly emerged deep learning methods, could remove the rain streaks well in many cases. However the essential factor in the generative procedure of the rain streaks, i.e., the motion blur, which leads to the line pattern appearances, were neglected by the deep learning rain streaks approaches and this resulted in over-derain or under-derain results. In this paper, we propose a novel rain streak removal approach using a kernel guided convolutional neural network (KGCNN), achieving the state-of-the-art performance with simple network architectures. We first model the rain streak interference with its motion blur mechanism. Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component. Then, after a dimensionality stretching operation, the learned motion blur kernel is stretched into a degradation map with the same spatial size as the rainy patch. The stretched degradation map together with the texture patch is subsequently input into a derain convolutional network, which is a typical ResNet architecture and trained to output the rain streaks with the guidance of the learned motion blur kernel. Experiments conducted on extensive synthetic and real data demonstrate the effectiveness of the proposed method, which preserves the texture and the contrast while removing the rain streaks.