Currently, the information processing in a spatial domain alone has intrinsic limitations that hinder the deep network's effectiveness (performance) improvement in a single image deraining. Moreover, the deraining networks' structures and learning processes are becoming increasingly intricate, leading to challenges in structural lightweight, and training and testing efficiency. We propose a lightweight multi-domain multi-attention progressive network (M2PN) to handle these challenges. For performance improvement, the M2PN backbone applies a simple progressive CNN-based structure consisting of the S same recursive M2PN modules. This recursive backbone with a skip connection mechanism allows for better gradient flow and helps to effectively capture low-to-high-level/scales spatial features in progressive structure to improve contextual information acquisition. To further complement acquired spatial information for better deraining, we conduct spectral analysis on the frequency energy distribution of rain steaks, and theoretically present the relationship between the spectral bandwidths and the unique falling characteristics and special morphology of rain steaks. We present the frequency-channel attention (FcA) mechanism and the spatial-channel attention (ScA) mechanism to fuse frequency-channel features and spatial features better to distinguish and remove rain steaks. The simple recursive network structure and effective multi-domain multi-attention mechanism serve as the M2PN to achieve superior performance and facilitate fast convergence during training. Furthermore, the M2PN structure, with a small network component quantity, shallow network channels, and few convolutional kernels, requires only 168K parameters, which is 1 to 2 orders of magnitude lower than the existing SOTA networks. The experimental results demonstrate that even with such a few network parameters, M2PN still achieves the best overall performance.

Many existing saliency detection methods made an assumption that the salient object is on the center of the image and incorporated such center-biased assumption in the design of their algorithms. Obviously, this is not always proper to set, especially for those imageries acquired by unmanned monitoring system or device (e.g., surveillance camera), in which the salient object could appear in any location within the image. Consequently, the resulted saliency detection performance could be greatly degraded. In this paper, an existing hypercomplex Fourier transform (HFT) based saliency detection algorithm is investigated and modified for improving the saliency detection performance. In details, we remove its prior assumption on `center bias' and exploit a location-aware strategy to identify the optimal saliency map across multiple scales of the image. Extensive simulation results have justified that the proposed location-aware HFT-based approach clearly outperforms existing five state-of-the-art algorithms on saliency detection.