Results 31 to 40 of about 441,411 (171)
Reference-Based Multi-Level Features Fusion Deblurring Network for Optical Remote Sensing Images
Remote Sensing, 2022 Blind image deblurring is a long-standing challenge in remote sensing image restoration tasks. It aims to recover a latent sharp image from a blurry image while the blur kernel is unknown.
Zhiyuan Li +4 more
doaj +1 more source
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 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.
Se Un Parka +5 more
core +1 more source
Blind Deblurring Using Internal Patch Recurrence [PDF]
, 2014 . Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g., superresolution from a single image).
Tomer Michaeli, Michal Irani
core +1 more source
Deep Regressor Networks for Blind Image Deblurring
, 2021 Image restoration concerns mainly smoothing noise and deblurring images that were corrupted either during acquisition or transmission. Since traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the ...
Rafael G. Pires +7 more
core +1 more source
Spectral Norm Regularization for Blind Image Deblurring
, 2021 Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be ...
Zhiyong Xu, Shuhan Sun, Jianlin Zhang
core +1 more source
Efficient, Blind, Spatially-Variant Deblurring for Shaken Images [PDF]
, 2014 International audienceIn this chapter we discuss modeling and removing spatially-variant blur from photographs. We describe a compact global parameterization of camera shake blur, based on the 3D rotation of the camera during the exposure. Our model uses
Ponce, Jean +3 more
core +2 more sources
Quantitative comparisons among blind deblurring methods.
, 2018 Quantitative comparisons among blind deblurring methods.
Houjie Li (4918960) +4 more
core +1 more source
Stochastic Blind Motion Deblurring [PDF]
, 2015 Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can therefore only be obtained with the help of prior information in the form of (often non ...
Heide, Felix +3 more
core +1 more source
Blind Image Deblurring via a Novel Sparse Channel Prior
Mathematics, 2022 Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (
Dayi Yang, Xiaojun Wu, Hefeng Yin
doaj +1 more source
Arbitrarily shaped Point Spread Function (PSF) estimation for single image blind deblurring [PDF]
, 2020 The research paper focuses on a challenging task faced in blind image deblurring (BID). It relates to the estimation of arbitrarily shaped (nonparametric or complex shaped) point spread functions (PSFs) of motion blur caused by camera handshake.
Khan, Aftab, Yin, Hujun; id_orcid
core +1 more source