This article addresses the problem of reconstructing a magnetic resonance image from highly undersampled data, which frequently arises in accelerated magnetic resonance imaging. We propose to impose sparsity of first and second order difference sparse coefficients within the complement of the known support. Second order variation is involved to overcome blocky effects and support information is used to reduce the sampling rate further. The resulting optimization problem consists of a data fidelity term and first-second order variation terms penalizing entries within the complement of the known support. The efficient split Bregman algorithm is used to solve the problem. Reconstruction results from magnetic resonance imaging data corresponding to different sampling rates are shown to illustrate the performance of the proposed method. Then, we also assess the tolerance of the new method to noise briefly. (2014) proposed a combined first and second order variation approach successively. The reconstruction quality of the latter method is not far off from that of TGV, and computational burden caused by numerical solution shows that TGV is, in general, about 10 times slower than the latter one.Methods mentioned above only exploit the sparsity which is implicit in MR images. Beyond utilizing sparsity, researchers pro-

. Significance: Effective vein visualization is critically important for several clinical procedures, such as venous blood sampling and intravenous injection. Existing technologies using infrared device or ultrasound rely on professional equipment and are not suitable for daily medical care. A regression-based vein visualization method is proposed. Aim: We visualize veins from conventional RGB images to provide assistance in venipuncture procedures as well as clinical diagnosis of some venous insufficiency. Approach: The RGB images taken by digital cameras are first transformed to spectral reflectance images using Wiener estimation. Multiple regression analysis is then applied to derive the relationship between spectral reflectance and the concentrations of pigments. Monte Carlo simulation is adopted to get prior information. Finally, vein patterns are visualized from the spatial distribution of pigments. To minimize the effect of illumination on skin color, light correction and shading removal operations are performed in advance. Results: Experimental results from inner forearms of 60 subjects show the effectiveness of the regression-based method. Subjective and objective evaluations demonstrate that the clarity and completeness of vein patterns can be improved by light correction and shading removal. Conclusions: Vein patterns can be successfully visualized from RGB images without any professional equipment. The proposed method can assist in venipuncture procedures. It also shows promising potential to be used in clinical diagnosis and treatment of some venous insufficiency.

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Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID‐19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near‐Infrared (Near‐Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process‐entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance.