Parallel iterative image restoration algorithms

2003 ◽  
Author(s):  
J.K. Paik ◽  
A.K. Katsaggelos
Keyword(s):  
Image Restoration ◽  
Parallel Iterative ◽  
Restoration Algorithms

Fast Adaptive Iterative Image Restoration Algorithms

1988 ◽  
Author(s):  
Serafim N. Efstratiadis ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Image Restoration ◽  
Restoration Algorithms

scholarly journals HST Image Restoration: Current Capabilities and Future Prospects

1994 ◽  
Vol 158 ◽  
pp. 61-69 ◽  
Author(s):  
Robert J. Hanisch ◽  
Richard L. White
Keyword(s):  
Image Restoration ◽  
Dynamic Range ◽  
Hubble Space Telescope ◽  
Spherical Aberration ◽  
Wide Field ◽  
Secondary Mirror ◽  
Faint Object ◽  
Spread Function ◽  
Restoration Algorithms ◽  
Space Variant

The spherical aberration in the primary mirror of the Hubble Space Telescope causes more than 80% of the light from a point source to be spread into a halo of radius of 2–3 arcsec. The point spread function (PSF) is both time variant (resulting from spacecraft jitter and desorption of the secondary mirror support structure) and space variant (owing to the Cassegrain repeater optics in the Wide Field / Planetary Camera). A variety of image restoration algorithms have been utilized on HST data with some success, although optimal restorations require better modeling of the PSF and the development of efficient restoration algorithms that accommodate a spacevariant PSF. The first HST servicing mission (December 1993) will deploy a corrective optics system for the Faint Object Camera and the two spectrographs and a second generation WF/PC with internal corrective optics. As simulations demonstrate, however, the restoration algorithms developed now for aberrated images will be very useful for removing the remaining diffraction features and optimizing dynamic range in post-servicing mission data.

scholarly journals Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Bo Liang ◽  
Xin-xin Jia ◽  
Yuan Lu
Keyword(s):  
Image Restoration ◽  
Color Image ◽  
Environmental Art ◽  
Special Effects ◽  
Structure Information ◽  
Film And Television ◽  
Restoration Algorithm ◽  
Restoration Method ◽  
Restoration Algorithms ◽  
Restoration Effect

Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.

Countering low visibility in video survey of an estuarine fish assemblage

2020 ◽  
Vol 26 (2) ◽  
pp. 190 ◽  
Author(s):  
James A. Donaldson ◽  
Paulo Drews Jr ◽  
Michael Bradley ◽  
David L. Morgan ◽  
Ronald Baker ◽  
...  
Keyword(s):  
Image Restoration ◽  
Species Level ◽  
Estuarine Fish ◽  
Tropical Estuaries ◽  
White Balance ◽  
Significant Difference ◽  
Low Visibility ◽  
Average Confidence ◽  
Video Survey ◽  
Restoration Algorithms

Sampling fish communities in tropical estuaries is inherently challenging due to poor visibility and the potential presence of dangerous fauna. We present two strategies for improving the identification of fishes in a turbid tropical estuary using video. The first was to attract species close to the camera by using two different bait types compared with no bait, and the second involved manipulating footage in the postfilming phase. No significant difference was found in the species richness recorded among camera bait treatments (thawed Australian sardines, canned sardines and unbaited), although baited cameras did detect 13 taxa not observed on the unbaited cameras. Three different image restoration algorithms (histogram equalisation, white balance and contrast-limited adaptive histogram equalisation) were compared in processing 22 instances where fish could not be confidently identified to species or genus level. Of these processed clips, five were able to be identified to species level by a panel of four coauthors. Further, two of the three algorithms yielded higher average confidence values for identification at the order, family, genus and species level than when the unprocessed footage was viewed. Image restoration algorithms can partly compensate for a reduction in image quality resulting from turbidity, addressing a key challenge for video-based sampling in estuaries.

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