15 November 2007 Infrared image pre-processing based on nonsubsampled contourlet transform
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Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 67871R (2007) https://doi.org/10.1117/12.750228
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Based on the nonsubsampled contourlet transform (NSCT) and two denoising models (i.e., fractional power model and cross-scale correlation model), an efficient pre-processing algorithm for infrared image is proposed. In our algorithm, the NSCT is used to decompose the image at different scale and orientation, and then implement pre-processing in the frequency domain, at last reconstruct coefficients to obtain ideal infrared image. The key of the proposed algorithm is pre-processing which includes noise removal and information enhancement. To reduce the two kinds of noises (i.e., Gaussian noise and shot noise) efficiently, the two models referred are applied to the NSCT coefficients respectively. The filtered results are fused to learn from the strong points of each denoising methods to offset the weakness of each other. Later, the denoised coefficients are classified to edges and noise and modified by a nonlinear mapping function. Experiments carried on infrared images show that the new algorithm can reduce the Gaussian noise and shot noise efficiently, while keeping the detail information well. Both in the objective performance index and subjective viewing assessment, the new algorithm is superior to the DWT-based method as well as the traditional method.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Junshan Li, Junshan Li, Xiongmei Zhang, Xiongmei Zhang, Kun Li, Kun Li, Xuhui Li, Xuhui Li, } "Infrared image pre-processing based on nonsubsampled contourlet transform", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67871R (15 November 2007); doi: 10.1117/12.750228; https://doi.org/10.1117/12.750228

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