Digital image restoration

By M. R. Banham, Aggelos K. Katsaggelos. IEEE Signal Processing Magazine, 2012

1,028 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e9db40e635e8e433abe74c2b1495e4e823052b1f

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A Survey of Human-in-the-loop for Machine Learning (2021, background): “Image restoration (IR) aims to recover the preliminary version of damaged images [95].”
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction (2008, methodology): “There are different approaches based on statistics [14, 5], Fourier and/or wavelet transforms [25, 31], or variational analysis [38, 7, 11] for image deblurring.”
Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013, methodology): “To verify the effectiveness of the proposed algorithm, the proposed R1TD tensor model is compared with several competitive methods: the channel-by-channel locally adaptive Wiener filter (CCWF) (Banham and Katsaggelos, 1997), channel-by-channel SVD (CCSVD) (Andrews and Patterson, 1976), SSAHTV (Yuan ”
RankIQA: Learning from Rankings for No-Reference Image Quality Assessment (2017, background): “ a range of very many human evaluators (commonly referred to as the Mean Opinion Score (MOS) [27,22]). IQA has been widely applied to problems where image quality is essential, like image restoration [13], image super-resolution [32], and image retrieval [37]. IQA approaches are generally divided in”
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems (2004, methodology): “Different FoRD techniques, such as LTI Wiener deconvolution [3], [4] and Tikhonov-regularized deconvolution [21], differ in their choice of shrinkage in (12).”
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration (2007, neutral): “[1], [5], [35].”
Perceptual visual quality metrics: A survey (2011, background): “In fact, blurring exists even in uncompressed images and video due to the imperfect PSF (point spread function) and out-of-focus of the imaging system, as well as object motion during the signal capturing process [3,39].”
A multi-frame image super-resolution method (2010, methodology): “Regularization-based approaches [9–11], learning-based SR method [14,18] , space-time SR method [15] and color image SR method [22,23] have been proposed.”