Symmetric Enhancement of Visual Clarity through a Multi-Scale Dilated Residual Recurrent Network Approach for Image Deraining

Images captured during rainy days present the challenge of maintaining a symmetrical balance between foreground elements (like rain streaks) and the background scenery. The interplay between these rain-obscured images is reminiscent of the principle of symmetry, where one element, the rain streak, overshadows or disrupts the visual quality of the entire image. The challenge lies not just in eradicating the rain streaks but in ensuring the background is symmetrically restored to its original clarity. Recently, numerous deraining algorithms that employ deep learning techniques have been proposed, demonstrating promising results. Yet, achieving a perfect symmetrical balance by effectively removing rain streaks from a diverse set of images, while also symmetrically restoring the background details, is a monumental task. To address this issue, we introduce an image-deraining algorithm that leverages multi-scale dilated residual recurrent networks. The algorithm begins by utilizing convolutional activation layers to symmetrically process both the foreground and background features. Then, to ensure the symmetrical dissemination of the characteristics of rain streaks and the background, it employs long short-term memory networks in conjunction with gated recurrent units across various stages. The algorithm then incorporates dilated residual blocks (DRB), composed of dilated convolutions with three distinct dilation factors. This integration expands the receptive field, facilitating the extraction of deep, multi-scale features of both the rain streaks and background information. Furthermore, considering the complex and diverse nature of rain streaks, a channel attention (CA) mechanism is incorporated to capture richer image features and enhance the model's performance. Ultimately, convolutional layers are employed to fuse the image features, resulting in a derained image. An evaluation encompassing seven benchmark datasets, assessed using five quality metrics against various conventional and modern algorithms, confirms the robustness and flexibility of our approach.