Leveraging intrinsic data priors is critical for effective data recovery. However, existing approaches often struggle to achieve theoretical guarantees, strong performance, and computational efficiency simultaneously. In this paper, we introduce a novel Representative Coefficient Correlated Total Variation (RCCTV) regularizer that captures the recently observed low-rank and local smoothness properties of the representative coefficient tensor derived from a low-rank decomposition. RCCTV regularizer offers three key advantages: (1) it operates on a compact representative coefficient image significantly smaller than the original data, enabling highly efficient optimization; (2) it jointly enforces low-rankness and spatial smoothness through a single regularizer, eliminating the need for trade-off parameters; and (3) when integrated into a robust PCA framework (i.e., RCCTV-RPCA model), it admits provable exact recovery under mild conditions. To solve the resulting model, we develop an efficient ADMM-based algorithm accelerated via fast Fourier transform. Extensive experiments on both synthetic and real-world datasets demonstrate that the RCCTV-RPCA model achieves state-of-the-art accuracy while running significantly faster. Our code and Supplementary Material are available at https://github.com/mendy-2013/RCCTV.

Industrial scene text detection is challenging due to cluttered backgrounds, rust occlusions, and arbitrary orientations. We introduce ISTD-DLA, a dynamic local-aware aggregation network for industrial text detection. The framework integrates two synergistic components: (i) a dynamic local-aware feature learner that fuses shape-aware and Bayar convolutions to enrich fine-grained structural cues; and (ii) a local feature aggregation module that forms superpixel-based proposals and uses cross-attention to iteratively exchange context between pixels and superpixels, enabling more precise localization in complex scenes. To maintain efficiency, we prune implicit-mapping submodules at inference, reducing complexity without degrading accuracy. On the MPSC benchmark, ISTD-DLA attains an F-measure of 86.2% at 32 FPS, demonstrating a favorable accuracy–efficiency trade-off and robust practicality for industrial applications.

Abstract There are still some challenges in the task of single image rain removal, such as artefact remnant, background over‐smooth, and increasingly complex and heavy‐weight network architecture. Especially too heavy‐weight network to fit outdoor detection devices or mobile devices. To address the above challenges, we propose a lightweight single image Deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The proposed network consists of five blocks and two convolution operations, where each block consists of a dilation convolution module and a convolutional block attention module (CBAM). Specifically, visual saliency module CBAM is used for accurate localization of rain streak, and further the combinations of dilated convolution with CBAM is used to extract feature maps of rain streaks faithfully, which is able to remove artefact remnant while maintaining background details. A good tradeoff is presented between the network's weight size and effect of rain removal. Specifically, with only 48,268 parameters, the proposed model can achieve a guaranteed performance. Extensive experiments on a few typical rainy scenarios on synthetic and real‐world datasets have demonstrated that to achieve the same level of performance, the proposed method has far smaller size than most of the baselines under both qualitative and quantitative analyses.