Low-light image enhancement improves the quality of video surveillance and image analysis and, as a result, has long been a hot topic in image processing. However, current research on this topic faces a difficult challenge—effectively suppressing noise while improving brightness and maintaining color consistency, especially in extremely dark scenes, where dark noise amplification, uneven exposure, and color shifts often interact, leading to detail loss and color distortion. To address the issue, we propose a dual-stage low-light enhancement framework based on the HVI (Horizontal/Vertical-Intensity) color space. The low-light image is first mapped to the HVI space, obtaining the intensity component I and the HVI-based feature map, with I being explicitly extracted as an intensity prior. A Transformer-based pre-recovery module is introduced for global dependency modeling, guided by the intensity prior I through an Intensity-Conditioned Block (ICB) for conditional feature interaction. Subsequently, a dual-branch enhancement network utilizes lightweight Complementary Cross-Attention (CCA) blocks for brightness refinement and color denoising. Finally, the enhanced image is remapped to the sRGB color space. The proposed framework decouples global brightness recovery and feature preprocessing from detail enhancement and color refinement, improving stability in extremely dark and high-noise scenarios. Through 18 quantitative and qualitative experiments, we demonstrate that our proposed method achieves superior performance in dark noise suppression and color restoration across multiple low-light datasets.

Coclustering approaches for grouping data points and features have recently been receiving extensive attention. In this paper, we propose a constrained dual graph regularized orthogonal nonnegative matrix trifactorization (CDONMTF) algorithm to solve the coclustering problems. The new method improves the clustering performance obviously by employing hard constraints to retain the priori label information of samples, establishing two nearest neighbor graphs to encode the geometric structure of data manifold and feature manifold, and combining with biorthogonal constraints as well. In addition, we have also derived the iterative optimization scheme of CDONMTF and proved its convergence. Clustering experiments on 5 UCI machine‐learning data sets and 7 image benchmark data sets show that the achievement of the proposed algorithm is superior to that of some existing clustering algorithms.

Extracting $k$-order maximal-sum principal submatrix from an $n$-order real matrix is a typical combinatorial optimization problem and an NP-hard problem. To improve the computational efficiency of solving this problem, we, in this paper, propose an accelerated algorithm with row-by-row updates, called the fusion row update accelerated algorithm, which works by reducing the number of addition operations for submatrix elements. The new algorithm is applied to accelerate color combination selection and maximize color difference, which improves the readability of data visualization results; it is also applied to accelerate stock investment portfolio selection and minimize correlation degree, which decreases the investment risk in the view of daily return volatility.