Background suppression of weak small targets in infrared image is the key of image tracking and monitoring, especially under the cloud background. In the area of imagesignalprocessing, there are a lot of background ...
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ISBN:
(纸本)9781510812055
Background suppression of weak small targets in infrared image is the key of image tracking and monitoring, especially under the cloud background. In the area of imagesignalprocessing, there are a lot of background suppression methods can be used to image filter by combining the characteristics of infrared image under the cloud background. In this paper, we introduce three typical background filter methods such as pulse median filter, multi-structural morphological filter and wavelet threshold method, realize them using MATLAB, and analyze their performances of background suppression of weak small targets in infrared image under the cloud background.
wavelet transform is a main tool for imageprocessingapplications in modern existence. A Double Density Dual Tree Discrete wavelet Transform is used and investigated for image denoising. images are considered for the...
wavelet transform is a main tool for imageprocessingapplications in modern existence. A Double Density Dual Tree Discrete wavelet Transform is used and investigated for image denoising. images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.
An efficient wavelet-based algorithm to reconstruct non-square/non-cubic signals from gradient data is proposed. This algorithm is motivated by applications such as image or video processing in the gradient domain. In...
An efficient wavelet-based algorithm to reconstruct non-square/non-cubic signals from gradient data is proposed. This algorithm is motivated by applications such as image or video processing in the gradient domain. In some earlier approaches, the non-square/non-cubic gradients were extended to enable a square/cubic Haar wavelet decomposition and the coarsest resolution subband was derived from the mean value of the signal. In this paper, a non-square/non-cubic wavelet decomposition is obtained directly without extending the gradient data. The challenge comes from finding the coarsest resolution subband of the wavelet decomposition and an algorithm to compute this is proposed. The performance of the algorithm is evaluated in terms of accuracy and computation time, and is shown to outperform the considered earlier approaches in a number of cases. Further, a closer look on the role of the coarsest resolution subband coefficients reveals a trade-off between errors in reconstruction and visual quality which has interesting implications in image and video processingapplications.
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