Image Processing Algorithms

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(→‎Image Dithering: clean up add a few things)
(→‎Image Denoising: add Color Banding section)
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*[http://iasir.net/IJETCASpapers/IJETCAS13-340.pdf A Review on Image Denoising Techniques.pdf] | [https://web.archive.org/web/20140911193010/http://iasir.net/IJETCASpapers/IJETCAS13-340.pdf mirror]
 
*[http://iasir.net/IJETCASpapers/IJETCAS13-340.pdf A Review on Image Denoising Techniques.pdf] | [https://web.archive.org/web/20140911193010/http://iasir.net/IJETCASpapers/IJETCAS13-340.pdf mirror]
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===[http://en.wikipedia.org/wiki/Colour_banding Color Banding]===
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*[http://www.ee.cuhk.edu.hk/~knngan/2011/TIP_v20_n8_p2110-2121.pdf Composite Model-Based DC Dithering for Suppressing Contour Artifacts in Decompressed Video.pdf] | [https://web.archive.org/web/20141201012218/http://www.ee.cuhk.edu.hk/~knngan/2011/TIP_v20_n8_p2110-2121.pdf mirror]
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*[http://www.cecs.uci.edu/~papers/icme05/defevent/papers/cr1737.pdf Flat-Region Detection ans False Contour Removal in the Digital TV Display] | [https://web.archive.org/web/20141201012503/http://www.cecs.uci.edu/~papers/icme05/defevent/papers/cr1737.pdf mirror]
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*[http://cilab.knu.ac.kr/seminar/Seminar/2010/20100403%20Multiscale%20Probabilistic%20Dithering%20for%20Suppressing%20Contour%20Artifacts%20in%20Digital%20Images.pdf Multi-Scale Probabilistic Dithering for Suppressing Contour Artifacts in Digital Images.pdf] | [https://web.archive.org/web/20141201011345/http://cilab.knu.ac.kr/seminar/Seminar/2010/20100403%20Multiscale%20Probabilistic%20Dithering%20for%20Suppressing%20Contour%20Artifacts%20in%20Digital%20Images.pdf mirror]
  
 
===Fractal and Wavelet Denoising===
 
===Fractal and Wavelet Denoising===

Revision as of 02:46, 1 December 2014

Contents

Image Dithering

Error Diffusion


Image Formats


Image Denoising

Color Banding

Fractal and Wavelet Denoising

NL Means

Salt and Pepper Noise


Image Inpainting


Image Scaling

Subpixel Image Scaling for Color Matrix Displays, Michiel A. Klompenhouwer, Gerard de Haan - Subpixel rendering’ algorithms are being used to convert an input image to subpixel-corrected display images. This paper deals with the consequences of the subpixel structure, and the theoretical background of the resolution gain. We will show that this theory allows a low-cost implementation in an image scaler. This leads to high flexibility, allowing different subpixel arrangements and a simple control over the trade-off between perceived resolution and color errors.
Discussion

Spline Scaling


Image Deblurring

  • A scaled gradient projection method for constrained image deblurring - A class of scaled gradient projection methods for optimization problems with simple constraints is considered. These iterative algorithms can be useful in variational approaches to image deblurring that lead to minimized convex nonlinear functions subject to non-negativity constraints and, in some cases, to an additional flux conservation constraint. A special gradient projection method is introduced that exploits effective scaling strategies and steplength updating rules, appropriately designed for improving the convergence rate. We give convergence results for this scheme and we evaluate its effectiveness by means of an extensive computational study on the minimization problems arising from the maximum likelihood approach to image deblurring. Comparisons with the standard expectation maximization algorithm and with other iterative regularization schemes are also reported to show the computational gain provided by the proposed method.
  • A Scaled Gradient Projection Method for Constrained Image Deblurring SBonettini, R Zanella and L Zanni - A class of scaled gradient projection methods for optimization problems with simple constraints is considered. These iterative algorithms can be useful in variational approaches to image deblurring that lead to minimize convex nonlinearfunctions subject to nonnegativity constraints and, in some cases, to an additional °ux conservation constraint. A special gradient projection method is introduced that exploits e®ective scaling strategies and steplength updating rules, appropriately designed for improving the convergence rate. We give convergence results for this scheme and we evaluate its e®ectiveness by means of an extensive computational study on the minimization problems arising from the maximum likelihood approach to image deblurring. Comparisons with the standard expectation maximization algorithm and with other iterative regularization schemes are also reported to show the computational gain provided by the proposed method.
    Discussion - Seems to need camera parameters, so may be a dead end.


Standardized Video Test Patterns

The colorbars values are listed below:

Rec. ITU-R BT.801-1
Description of encoded colour-bar signals according to the 4:2:2 level
of Recommendation ITU-R BT.601
100/0/75/0 colour bars
color		Y	Cb	Cr
white		235	128	128
yellow		162	 44	142
cyan		131	156	 44
green		112	 72	 58
magenta		 84	184	198
red		 65	100	212
blue		 35	212	114
black		 16	128	128

Description of encoded colour-bar signals according to the 4:2:2 level
of Recommendation ITU-R BT.601
100/0/100/0 colour bars
AND
Rec. ITU-R BT.1729
Appendix 2
100% colorbars
color		Y	Cb	Cr
white		235	128	128
yellow		210	 16	146
cyan		170	166	 16
green		145	 54	 34
magenta		106	202	222
red		 81	 90	240
blue		 41	240	110
black		 16	128	128

Discussion of standards references

SuperResolution


Image Registration


Deinterlacing


Image Rotation

These are based on the fast 3 shear methods:

First shear : x' = x - tan (theta/2) * y
Second shear : y' = y + sin(theta) * x
Third shear : x' = x - tan (theta/2) * y


Seam Carving


High Dynamic Range (HDR)


TODO

  • move to different section and category
  • fixed all dead links
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