Results 21 to 30 of about 2,330,920 (308)
Mathematics, 2023 A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis.
Noor Ain Syazwani Mohd Ghani +5 more
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A Characterization of the Domain of Beta-Divergence and Its Connection to Bregman Variational Model
Entropy, 2017 In image and signal processing, the beta-divergence is well known as a similarity measure between two positive objects. However, it is unclear whether or not the distance-like structure of beta-divergence is preserved, if we extend the domain of the beta-
Hyenkyun Woo
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Euler's elastica and curvature based model for image restoration. [PDF]
PLoS ONE, 2018 Minimization functionals related to Euler's elastica energy has a broad range of applications in computer vision and image processing. This paper proposes a novel Euler's elastica and curvature-based variational model for image restoration corrupted with
Mushtaq Ahmad Khan +3 more
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Sobolev spaces with non-Muckenhoupt weights, fractional elliptic operators, and applications [PDF]
, 2018 We propose a new variational model in weighted Sobolev spaces with non-standard weights and applications to image processing. We show that these weights are, in general, not of Muckenhoupt type and therefore the classical analysis tools may not apply ...
Antil, Harbir, Rautenberg, Carlos N.
core +3 more sources
An Efficient Method for Euler’s Elastica Based Image Deconvolution
IEEE Access, 2019 Variational models involving Euler's elastica energy have a wide range of applications in digital image processing. Recently, fast methods, such as the proximal-augmented Lagrangian method (PALM), have been successfully used to solve nonlinear higher ...
Samad Wali +6 more
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Fast Image Processing with Fully-Convolutional Networks [PDF]
IEEE International Conference on Computer Vision, 2017 We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator’s action.
Qifeng Chen, Jia Xu, V. Koltun
semanticscholar +1 more source
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network
Sensors, 2020 A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array.
Yunjin Park +3 more
doaj +1 more source
Feature Reconstruction from Incomplete Tomographic Data without Detour
Mathematics, 2022 In this paper, we consider the problem of feature reconstruction from incomplete X-ray CT data. Such incomplete data problems occur when the number of measured X-rays is restricted either due to limit radiation exposure or due to practical constraints ...
Simon Göppel +2 more
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Unsupervised Transformer Balanced Hashing for Multispectral Remote Sensing Image Retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 For remote sensing (RS) image retrieval task, hashing technology have been extensively researched in recent works. Unsupervised hashing approaches have attracted much attention in the RS data processing field because label collection takes a lot of time.
Yaxiong Chen +3 more
doaj +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2013 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida +39 more
core +5 more sources