Results 1 to 10 of about 2,035,117 (267)
Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution [PDF]
Sensors, 2023 Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways
Jongeun Park, Hansol Kim, Moon Gi Kang
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CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation [PDF]
Sensors, 2020 With the improvement of semiconductor technology, the performance of CMOS Image Sensor has been greatly improved, reaching the same level as that of CCD in dark current, linearity and readout noise. However, due to the production process, CMOS has higher
Tao Zhang +3 more
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Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing
Remote Sensing, 2022 Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels.
Xinxi Feng, Le Han, Le Dong
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Deep Unfolding Network for Multi-Band Images Synchronous Fusion
IEEE Access, 2023 This study proposes a new deep neural network to solve the multi-band image synchronous fusion problem (MBF-Net). Unlike other deep learning-based methods, our network architecture design combines the ideas of model-driven and data-driven methods, so it ...
Dong Yu +4 more
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Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging.
Yin Zhang +4 more
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Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling
Remote Sensing, 2023 With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce
Takashi Fuse, Kazuki Imose
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Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise
Frontiers in Computer Science, 2023 In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k-means clustering to segment the image. Specifically for
Kevin Bui +3 more
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Directional Total Variation [PDF]
IEEE Signal Processing Letters, 2012 This paper introduces a “directional total variation” (TV) where the gradients are weighted depending on their direction. The introduced directional TV has increased (and tunable) sensitivity to variations at a selected direction. In order to demonstrate the utility of the directional TV, we consider an image denoising formulation.
Ilker Bayram, Mustafa E. Kamasak
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Total Variation as a Local Filter [PDF]
SIAM Journal on Imaging Sciences, 2011 In the Rudin-Osher-Fatemi (ROF) image denoising model, total variation (TV) is used as a global regularization term. However, as we observe, the local interactions induced by TV do not propagate much at long distances in practice, so that the ROF model is not far from being a local filter.
Louchet, Cécile, Moisan, Lionel
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Sensors, 2022 Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image.
Xiangxiang Jia, Baofeng Guo
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