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8 December 2011 Infrared image denoising and enhancing algorithm using adaptive threshold shrinkage in a new contourlet transform
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Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 80020V (2011) https://doi.org/10.1117/12.901532
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
Edge and detail of infrared image are blurry or loss after denoising with the threshold shrinkage arithmetic. A new adaptive denoising and enhancing algorithm with detail enhancement based on a new Contourlet Transform with Sharp Frequency Localization(CT-SFL) is proposed to preserve the edge better. CT-SFL has the characteristic of well-localized in the frequency domain compared with the original contourlet. Firstly, CT-SFL, instead of the original contourlet, is employed as the multiscale decompositon to decompose the infrared image into subbands. Secondly, the hierarchical adaptive denoising threshold of new Contourlet coefficient is estimated respectively by each location from different scale and directional subband, the noisy image is denoising with soft threshold related to the transform scale and direction, then the denosing image is enhanced by taking decomposable scale and directional energy into account with intrasubband and interscale dependencies. Thirdly, inverse CT-SFL is used to reconstruct the denoising and enhancing image. Finally, in order to reduce significant amount of aliasing components which are located far away from the desired support because of the new Contourlet Transform, cycle spinning is accomplished to the whole denoising and enhancement process to overcome the lack of translation invariance property and suppress pseddo-Gibbs phenomena around singularities of denoising image. Numerical experiments on infrared noisy image show that the proposed novel algorithm can significantly outperform some arithmetics based on contourlet like 3 sigma, VisuShrink and Bayes Shrinkage in all kinds of noise spectral density both in terms of PSNR(by several dB) and in visual quality, which can enhance image's detail and stretch its contrast with nearly similar computational complexity.
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Fei Wang, Xiaogeng Liang, Yankai Cui, and Gang Liu "Infrared image denoising and enhancing algorithm using adaptive threshold shrinkage in a new contourlet transform", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80020V (8 December 2011); https://doi.org/10.1117/12.901532
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