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Sparse method for ringing artifact detection

P. 662–667.
Umnov A., Nasonov A. V., Krylov A. S., Ding Y.

The problem of non-reference ringing detection is considered. The idea of the proposed method is to decompose the input edges into the sums of blurred edge, ringing oscillations and the residual using sparse representation approach with the pre-generated dictionary. Edges with ringing effect are modeled by applying ideal low-pass filter to the step edge with different cut-off frequencies. Then they are separated into edge and ringing components by subtracting the ideal step edge blurred with Gaussian filter. The shifted and rotated blur and ringing components form the dictionary used for sparse representation. The presented method performs well on images with ringing from different source types and is robust for the images with noise.

Language: English
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Keywords: sparse codingringing artifact

In book

2014 12th International Conference on Signal Processing (ICSP)
United States of America: IEEE, 2014.
Similar publications
Research of sparse representation method for ringing suppression
Umnov A., Krylov A. S., Computer Optics 2016 Vol. 40 No. 6 P. 895–903
In this paper we suggest an algorithm for ringing suppression based on a sparse  representation method. As one of its steps, the suggested method includes image deblurring  based on the Wiener-Hunt deconvolution algorithm. The ringing suppression algorithm uses  the signals' mutual coherence and sparsities analysis when dealing with the ringing effect  based on the sparse representation method. We also ...
Added: October 21, 2017
Sparse Approach to Image Ringing Detection and Suppression
Umnov A., Krylov A. S., Pattern Recognition and Image Analysis 2017 Vol. 27 No. 4 P. 754–762
In this work we discuss methods for image ringing detection and suppression that are based on the sparse representations approach and suggest a new ringing suppression method. The ringing detection algorithm is based on construction of the synthetic dictionary that is used to represent ringing effect as a sum of blurred edge and pure ringing ...
Added: October 17, 2017
Influence of GIBBS Phenomenon on the Mutual Coherence in Sparse Representations
Krylov A. S., Umnov A., Moscow University Computational Mathematics and Cybernetics 2016 Vol. 40 P. 155–160
The influence of ringing effect (which is based on the Gibbs phenomenon) on the mutual coherence in sparse representation approach is considered. It is shown that for random vectors, the ringing effect increases the average mutual coherence. Numerical results that demonstrate that the mutual coherence behaves identically for real images are given. It is also ...
Added: October 17, 2016
Влияние эффекта Гиббса на взаимную согласованность в методе разреженных представлений для изображений
Крылов А. С., Umnov A., Вестник Московского университета. Серия 15: Вычислительная математика и кибернетика 2016 Т. 4 С. 10–15
В работе исследовано влияние эффекта ложного оконтуривания (в основе которого лежит эффект Гиббса) на взаимную согласованность в методе разреженных представлений. Доказано, что для случайных векторов эффект ложного оконтуривания в среднем увеличивает взаимную согласованность. Приведены результаты численных экспериментов, демонстрирующие, что взаимная согласованность для блоков реальных изображений ведет себя аналогичным образом. Показано влияние эффекта ложного оконтуривания на ...
Added: October 17, 2016
Ringing Artifact Suppression Using Sparse Representation
Alexey V. Umnov, Krylov A., Nasonov A., Lecture Notes in Computer Science 2015 Vol. 9386 P. 35–45
The article refers to the problem of ringing artifact suppression. The ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for ringing artifact suppression after Fourier cut-off filtering. It can be also used for image deringing in the case of ...
Added: October 14, 2015
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