Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception.
Due to independent and coarse quantization of transform coefficients in each block, block-based transform coding usually introduces visually annoying blocking artifacts at low bitrates, which greatly prevents further bit reduction. To alleviate the conflict between bit reduction and quality preservation, deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking using constrained non-convex low-rank model is proposed. The ℓ(p) (0 < p < 1) penalty function is extended on singular values of a matrix to characterize low-rank prior model rather than the nuclear norm, while the quantization constraint is explicitly transformed into the feasible solution space to constrain the non-convex low-rank optimization. Moreover, a new quantization noise model is developed, and an alternatively minimizing strategy with adaptive parameter adjustment is developed to solve the proposed optimization problem. This parameter-free advantage enables the whole algorithm more attractive and practical. Experiments demonstrate that the proposed image deblocking algorithm outperforms the current state-of-the-art methods in both the objective quality and the perceptual quality.
Abstract-This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.
We aimed to identify if maternal and infant factors were associated with neutral human milk oligosaccharides (HMOs) variability and examined the associations between HMOs concentration and infant growth and disease status in healthy Chinese mothers over a 6-month lactation period. We recruited mothers and their full-term infants as our subjects. At 1–5 days, 8–14 days, 4 weeks, and 6 months postpartum, all participants were interviewed to collect breast milk samples, obtain follow-up data and measure infant length and weight at their local hospital. A total of 23 neutral HMOs were analyzed by high performance liquid chromatography (HPLC)- mass spectrometer (MS). Secretor and Lewis phenotype were determined by the concentration of 2′-fucosyllactose (2′-FL) and Lacto-N-fucopentaose (LNFP)-II. The associations between maternal and infant factors with HMOs concentrations were investigated. A total of 464 human breast milk samples were collected from 116 mothers at four different time points. In total, 76.7% mothers were found to be Secretor and Lewis positive phenotype (Se+Le+), 17.2% were Se-Le+, 4.3% were Se+Le-, and 1.7% were Se-Le-. Several individual HMOs, including 2′-FL, Lactodifucotetraose (LDFT), LNFP-I were determined by Secretor phenotype. Most individual HMOs decreased at the later stage of lactation, except 3′-FL. We suggest that Secretor phenotype and lactation stage could influence most of the neutral HMOs. Concentrations of specific HMOs may be associated with maternal age, allergic history, pre-pregnancy body mass index (BMI), parity, delivery mode, infant gestational age and gender.
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