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Many video denoising methods originated from methods designed for processing static two-dimensional images. Videos would be processed frame by frame, a process with a relatively high computational complexity, without taking into account the correlation information between frames. In this paper, a video denoising method using coefficient shrinkage and threshold adjustment based on Surfacelet transform (CSTA-ST) is proposed, which processes multiple frames of a video as an ensemble. Spatial correlation is used to define a weighted spatial energy. Each Surfacelet transform (ST) coefficient has a corresponding estimated energy value, in which the ST coefficients are grouped by. The similarity of the ST coefficients in a group determines the threshold of each ST coefficient. In addition, according to the neighborhood information of ST coefficients, the threshold is adjusted by a threshold adjustment factor. The coefficient shrinkage parameter is determined based on the adjusted threshold, and the ST coefficients are shrunk. Finally, the denoised video is obtained by the inverse ST using the shrunk coefficients. In experiments, video sequences with noise are tested, and the denoised results of the proposed method are compared with that of current denoising methods. The experimental results show that the proposed method significantly improves the peak signal-to- noise ratio (PSNR) and the structural similarity (SSIM) for various levels of noise and motion, and the ideal denoised visual effect is obtained.  相似文献
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A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.  相似文献
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In this paper, a new fusion rule based on a pulse coupled neural network (PCNN) and the clarity of images is proposed for multi-band synthetic aperture radar (SAR) image fusion. By using a stationary wavelet-based nonsubsampled contourlet transform (SW-NSCT), we can calculate a flexible multiscale, multidirectional, anisotropy and shift-invariant representation of registered SAR images. A weighted fusion rule is performed on the low frequency subbands to calculate the fused lowpass band. For the fusion of high frequency directional subband images, a PCNN model is constructed, where the linking strength of each neuron is determined by the clarity of the decomposed subband images. The fusion approach exploits the advantages of both SW-NSCT in multiscale geometric representations and that of PCNN in the determination of fusion rules; as predicted, the obtained fusion image can preserve much more information regarding textures and edges of the images, compared to its counterparts. Some experiments are performed by comparing the new algorithm with other existing fusion rules and methods. The experimental results show that the proposed fusion approach is effective and can provide better performance in fusing multi-band SAR images than some current methods.  相似文献
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