Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.

The stimulus response of the classical receptive field (CRF) of neuron in primary visual cortex is affected by its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition, which depends primarily on the correlation of both visual stimulations. The theory of periphery and center interaction with visual characteristics can be applied in night vision information processing. In this paper, a weighted kernel principal component analysis (WKPCA) degree of homogeneity (DH) amended inhibition model inspired by visual perceptual mechanisms is proposed to extract salient contour from complex natural scene in low-light-level image. The core idea is that multifeature analysis can recognize the homogeneity in modulation coverage effectively. Computationally, a novel WKPCA algorithm is presented to eliminate outliers and anomalous distribution in CRF and accomplish principal component analysis precisely. On this basis, a new concept and computational procedure for DH is defined to evaluate the dissimilarity between periphery and center comprehensively. Through amending the inhibition from nCRF to CRF by DH, our model can reduce the interference of noises, suppress details, and textures in homogeneous regions accurately. It helps to further avoid mutual suppression among inhomogeneous regions and contour elements. This paper provides an improved computational visual model with high-performance for contour detection from cluttered natural scene in night vision image.