Iterative Image Rain Removal Network Using Consecutive Residual Long Short-Term Memory

Image rain removal is designed to effectively separate rain streaks from the background image layer. However, rain streaks in real-world scenarios vary in density, shape, and direction, making it difficult to decompose rainy images into clean backgrounds and rain layers. In this study, we introduce an iterative framework for image deraining to progressively enhance rainy images using a residual long short-term memory structure. The overall network comprises a multidomain residue channel, a fusion module, and a consecutive residual long short-term memory. We introduce multidomain residue channels by computing them in both the image and wavelet low-frequency domains. We propose a fusion module to combine the residue channel for guidance with wavelet domain features for rain removal. We also propose a feature extraction module based on successive residual long short-term memory to extract the main features in the wavelet domain. An iterative image restoration framework comprising three primary modules is introduced to progressively enhance rainy images. To evaluate the performance of the proposed approach, we conduct experiments using widely used benchmarks. The results demonstrate that our method outperforms state-of-the-art methods in image rain removal.