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Image restoration

About: Image restoration is a(n) research topic. Over the lifetime, 23420 publication(s) have been published within this topic receiving 509518 citation(s).

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Papers
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Open accessJournal ArticleDOI: 10.1016/0004-3702(81)90024-2
Abstract: Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.

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Topics: Image restoration (59%), Optical flow (59%), Feature detection (computer vision) (58%) ...read more

10,249 Citations


Open accessBook
Anil K. Jain1Institutions (1)
03 Oct 1988-
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.

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Topics: Image restoration (76%), Standard test image (74%), Image processing (73%) ...read more

8,403 Citations


Open accessBook
01 Dec 2003-
Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.

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Topics: Digital image processing (75%), Image processing (74%), Standard test image (70%) ...read more

6,204 Citations


Open accessJournal ArticleDOI: 10.1137/040616024
Abstract: The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics In spite of the sophistication of the recently proposed methods, m

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  • Fig. 6.10. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 25), the total variation minimization, the Tadmor et al. iterated total variation, the Osher et al. iterated total variation.
    Fig. 6.10. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 25), the total variation minimization, the Tadmor et al. iterated total variation, the Osher et al. iterated total variation.
  • Fig. 6.9. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 25), the DCT empirical Wiener filter, Hard TIWT and NL-means algorithm.
    Fig. 6.9. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 25), the DCT empirical Wiener filter, Hard TIWT and NL-means algorithm.
  • Fig. 2.2. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 20), gaussian convolution, anisotropic filter, total variation minimization, Tadmor et al. iterated total variation, Osher et al. iterated total variation and the Yaroslavsky neighborhood filter.
    Fig. 2.2. Denoising experience on a natural image. From left to right and from top to bottom: noisy image (standard deviation 20), gaussian convolution, anisotropic filter, total variation minimization, Tadmor et al. iterated total variation, Osher et al. iterated total variation and the Yaroslavsky neighborhood filter.
  • Fig. 5.7. Optimal correction experience. Left: Noisy image. Middle: NL-means solution. Right: NL-means corrected solution. The average with the noisy image makes the solution to be noisier, but details and fine structure are better preserved.
    Fig. 5.7. Optimal correction experience. Left: Noisy image. Middle: NL-means solution. Right: NL-means corrected solution. The average with the noisy image makes the solution to be noisier, but details and fine structure are better preserved.
  • Fig. 5.6. NL-means denoising experiment with a color image. Left: Noisy image with standard deviation 15 in every color component. Right: Restored image.
    Fig. 5.6. NL-means denoising experiment with a color image. Left: Noisy image with standard deviation 15 in every color component. Right: Restored image.
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3,859 Citations


Journal ArticleDOI: 10.1109/42.363108
H.M. Hudson1, R.S. Larkin1Institutions (1)
Abstract: The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained. >

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3,588 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202214
2021835
2020951
2019954
2018857
2017989

Top Attributes

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Topic's top 5 most impactful authors

Aggelos K. Katsaggelos

177 papers, 8.3K citations

Joonki Paik

121 papers, 1.2K citations

Norman S. Kopeika

63 papers, 1K citations

Rafael Molina

56 papers, 1.8K citations

A. N. Rajagopalan

29 papers, 570 citations

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