Shape-Adaptive Transforms Filtering
Pointwise SA-DCT algorithms


Shape-Adaptive DCT Denoising

In these pages we provide access to the material (software, publications, experimental data, presentations, test-images, etc.) related to our recent research in the field of shape-adaptive transforms for image filtering.

Introduction

Software


Photoshop Plug-ins
ver. 1.00 released 18 january 2008

Matlab Demobox
ver. 1.43 released 15 May 2016

Results

Publications


Shape-Adaptive DCT Denoising
Examples of the adaptive-shape supports used for SA-DCT domain filtering.


Introduction


The two-dimensional separable DCT, computed on a square or rectangular support, is a well established and very efficient transform in order to achieve a sparse representation of image blocks. For natural images, its decorrelating performance is close to that of the optimum Karhunen-Loeve transform. Thus, the DCT has been successfully used as the key element in many compression and denoising applications. However, in presence of edges such near-optimality fails. Because of the lack of sparsity, edges cannot be coded or restored effectively, and ringing artifacts arising from the Gibbs phenomenon become visible.

We provide powerful image filtering algorithms based on shape-adaptive transforms.
By using arbitrarily-shaped supports which are adaptive with respect to the image, the reconstructed edges are clean, and no unpleasant ringing artifacts are introduced by the fitted transform. In particular, we exploit the low-complexity shape-adaptive DCT (SA-DCT) to realize very efficient solutions to a wide class of image restoration problems (e.g. denoising, deblurring, deringing and deblocking).
Experiments demonstrate a state-of-the-art quality of the final estimate, both in terms of objective criteria and visual appearance.

A web-presentation introducing the approach is available here.


Software


We follow the principle of “reproducible research” and make our software routines available free-of-charge for educational and non-profit scientific research, enabling others researchers to understand and reproduce our work.


Adobe Photoshop Adobe Photoshop Plug-ins
The Pointwise Shape-Adaptive DCT Plug-ins for Adobe Photoshop provide filters for:
   - grayscale and color image denoising
   - JPEG deblocking and deringing

ReadMe.txt
Information and instructions		 Pointwise Shape-Adaptive DCT Plug-ins
for Adobe Photoshop

download zip package
(ver. 1.00, released 18 january 2008)
Adaptive-Shape Explorer


Matlab Matlab Demobox
The Pointwise Shape-Adaptive DCT Demobox provides Matlab routines for:
   - grayscale and color image denoising
   - image deblurring
   - JPEG deblocking and deringing
   - inverse halftoning

The routines are based on the low-complexity Shape-Adaptive DCT (SA-DCT) transform.

ReadMe.txt
what's new?		 Pointwise Shape-Adaptive DCT Demobox
(for Matlab 7.5 or later)

download zip package
(ver. 1.43, released 15 May 2016)

Latest release includes the
Pointwise SA-DCT inverse-halftoning
and Pointwise SA-DCT JPEG deblocking filters 		Adaptive-Shape Explorer


This software is based on (and requires) the LASIP anisotropic nonparametric image restoration demobox.


Any unauthorized use of the provided software for industrial or profit-oriented activities is expressively prohibited. By downloading any of the files contained in this site, you implicitly agree to all the terms of the TUT limited license. Please read the TUT limited license PDF before you proceed with downloading any of the files.



Results

noisy filtering denoised
noisy filtering denoised
JPEG filtering deblocked
blurred filtering deblurred
Denoising Color denoising Deblocking Deblurring

Some results (images and tables) which can be obtained using the proposed Pointwise Shape-Adaptive DCT filtering technique can be found here.



Publications

2007

PDF Foi, A., Pointwise shape-adaptive DCT image filtering and signal-dependent noise estimation, Tampere University of Technology, Publication 710, ISBN 978-952-15-1922-2, December 2007.
PDF Foi, A., V. Katkovnik, and K. Egiazarian, “Signal-dependent noise removal in Pointwise Shape-Adaptive DCT domain with locally adaptive variance”, Proc. 15th European Signal Process. Conf., EUSIPCO 2007, Poznan, September 2007.
PDF Foi, A., V. Katkovnik, and K. Egiazarian, “Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images”, IEEE Trans. Image Process., vol. 16, no. 5, pp. 1395-1411, May 2007.

2006

PDF Foi, A., and V. Katkovnik, “From local polynomial approximation to pointwise shape-adaptive transforms: an evolutionary nonparametric regression perspective”, Proc. Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2006, Florence, September 2006.
PDF Dabov, K., A. Foi, V. Katkovnik, and K. Egiazarian, “Inverse halftoning by pointwise shape-adaptive DCT regularized deconvolution”, Proc. Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2006, Florence, September 2006.
PDF Foi, A., V. Katkovnik, and K. Egiazarian, “Pointwise Shape-Adaptive DCT for high-quality deblocking of compressed color images”, Proc. 14th European Signal Process. Conf., EUSIPCO 2006, Florence, September 2006.
PDF Foi, A., V. Katkovnik, and K. Egiazarian, “Pointwise Shape-Adaptive DCT Denoising with Structure Preservation in Luminance-Chrominance Space”, Proc. of the 2nd Int. Workshop on Video Process. and Quality Metrics for Consumer Electronics, VPQM2006, Scottsdale, 2006.
PDF Foi, A., K. Dabov, V. Katkovnik, and K. Egiazarian, “Shape-Adaptive DCT for Denoising and Image Reconstruction”, Proc. SPIE Electronic Imaging 2006, Image Processing: Algorithms and Systems V, 6064A-18, San Jose, 2006.

2005

PDF Foi, A., V. Katkovnik, and K. Egiazarian, “Pointwise Shape-Adaptive DCT as an overcomplete denoising tool”, Proc. Int. TICSP Workshop Spectral Meth. Multirate Signal Process., SMMSP 2005, Riga, 2005.


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