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Abstract: A poor inherent resolution capability of the passive millimeter-wave (PMMW) imaging becomes a problem in many applications. This paper proposes a parametric blind deconvolution approach for improving the resolution of PMMW images with low signal-to-noise ratio. Image deconvolution is a challenging ill-posed inverse problem when only partial knowledge of the point spread function (PSF) is available, therefore, regularization techniques need to be used. To restore high quality PMMW image, framelet based regularization constraint is incorporated into parametric blind PMMW image deconvolution framework, which is modeled as an alternative optimization problem about the image and the PSF. The PSF is modeled as a parametric form to restrict the PSF solution space. Furthermore, the split Bregman iteration is used to solve the resulting minimization problem. Comparative experimental results on simulated and real PMMW images show that the proposed method can effectively suppress noise, reduce artifacts, and improve the spatial resolution. [Copyright &y& Elsevier]

Abstract: In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image. [Copyright &y& Elsevier]

Imaging from a degenerated push broom scanner usually leads to an undesired stripe noise which seriously affected the image quality. The original unidirectional total variation ( UTV ) model produces a poor performance on stripe images which has a strong contrast between cleaning image area and striped area or different image area. In this research, a new destriping method which combines TV - Stokes and UTV model ( UTV-Stokes for short) has been developed to overcome the disadvantages of UTV . By distinguishing stripe region and no-stripe region in calculation, this method can avoid excessive smoothing or residual's appearing through less filtering. Comparative results on simulated and real striped images taken with MODIS and hyperspectral imaging systems demonstrated that the proposed method not only can handle various stripe images with different noise intensity but also can preserve the edge and detailed information. [ABSTRACT FROM AUTHOR]

Abstract: Multi-detectors imaging system often suffers from the problem of the stripe noise, which greatly degrades the quality of the resulting images. To better remove stripe noise and preserve the edge and texture information, a robust destriping algorithm with spatially adaptive unidirectional total variation (SAUTV) model is introduced. The spatial information of the striping noise is detected by using the stripe indicator called difference eigenvalue, and a weighted parameter determined by the difference eigenvalue information is added to constrain the regularization strength of the UTV regularization. The proposed algorithm can effectively remove the stripe noise and preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Split Bregman method is utilized to efficiently solve the resulting minimization problem. Comparative results on simulated and real striped images taken with two kinds of imaging systems are reported. [Copyright &y& Elsevier]

Abstract: The paper presents a versatile nonlinear diffusion method to visually enhance the angiogram images for improving the clinical diagnosis. Traditional nonlinear diffusion has been shown very effective in edge-preserved smoothing of images. However, the existing nonlinear diffusion models suffer several drawbacks: sensitivity to the choice of the conductance parameter, limited range of edge enhancement, and the sensitivity to the selection of evolution time. The new anisotropic diffusion we proposed is based on facet model which can solve the issues mentioned above adaptively according to the image content. This method uses facet model for fitting the image to reduce noise, and uses the sum square of eigenvalues of Hessian as the standard of the conductance parameter selection synchronously. The capability of dealing with noise and conductance parameter can also change adaptively in the whole diffusion process. Moreover, our method is not sensitive to the choice of evolution time. Experimental results show that our new method is more effective than the original anisotropic diffusion. [Copyright &y& Elsevier]

Abstract Pacific saury (Cololabis saira) are widely distributed in northwestern Pacific, migrating from their spawning ground in subtropical Kuroshio waters south of Japan to feeding grounds in Oyashio waters northeast of Japan. The abundance of Pacific saury is expected to be affected not only by environmental conditions in the fishing grounds of the Oyashio region, but also by recruitment processes influenced by environmental conditions in the Kuroshio region. In this study, we focus on the effects of environmental variations in the spawning ground on Pacific saury abundance, approximated by catch and catch per unit effort (CPUE) data. To examine interannual-decadal variability in the spawning ground, we developed a suite of spawning ground indices, including (1) average winter sea surface temperature (WSST), (2) the meridional position of 19 °C sea surface temperature (SST) isoline (MP19), and (3) SST-suitability-weighted size of spawning ground (WSSG), in the Kuroshio region. These spawning ground indices exhibited interannual-decadal variation patterns with regime shifts in 1962/63, 1976/77, 1987/88, 1997/98, and likely in the early 2010s, which corresponded well to data on catch and CPUE of Pacific saury. Large scale climatic indices such as Southern Oscillation Index (SOI) and Asian Monsoon Index (MOI) were negatively correlated with winter SST in most of the Kuroshio region, suggesting that large-scale climatic influences played important roles in the variability of SST within the Kuroshio region. Gradient forest analyses were used to quantify the importance of these spawning ground indices for explaining the variations of Pacific saury abundance and to identify shifts in catch and CPUE along the gradients of the spawning ground indices. MP19 with a 2-year lag (MP19_Lag2) was identified as the most important predictor of Pacific saury abundance in terms of CPUE, followed by WSST_Lag2, WSST, WSSG_Lag1, and WSSG. Spawning ground indices, particularly MP19_Lag2, were useful for rationalizing the dynamics of Pacific saury abundance, matching well the striking declines of catch both in the early 1960s and also in the most recent years. Highlights • Spawning ground indices were estimated to analyze the effects on Pacific saury. • Spawning ground indices showed synchronous regime shifts with Pacific saury. • MP19 and WSSG well explained the dramatic decline in catch of Pacific saury. • Climate-induced variations in spawning ground affect Pacific saury abundance. [ABSTRACT FROM AUTHOR]