Mallat, "Image compression with geometrical wavelets," in Proceeding of the International Conference on Image Processing, Vancouver, Septembre 2000.Pennec EL,Mallat S.Image compression with geometrical wavelets.Proceedings of the IEEE International Conference on Image Processing. 2000...
We propose a novel method for simultaneous speckle reduction and data compression based on wavelets. The main feature of the method is that of preserving the geometrical shapes of the figures present in the noisy images. A fast algorithm, the dynamic perceptron, is applied to detect the regular...
Efficient image compression using directionlets We combine the directionlets with the space-frequency quantization (SFQ) image compression method, originally based on the standard two-dimensional wavelet ... V Velisavljevic,B Beferull-Lozano,M Vetterli,... - International Conference on Information 被...
The simulation results show that, the extended shearlet based compression technique is more efficient than EPWT coding technique for wide range of geometrical features of the images. Quantitative analysis on standard test images show that the proposed technique outperforms the EPWT coding technique by...
data sets. We note that finding optimized directional wavelets in the axial direction relies on similar concepts as those of motion-compensated temporal filtering, which was very popular in wavelet-based coding of video[36],[37],[38],[39],[40]. The state-of-the-art in video compression,...
different denoising techniques like wavelet thresholding, robust statistics, and variation methods are used. In pathology microscopic images, noise is produced duringstaining, image acquisition,image compression, and filtering and reconstruction phases. Other image denoising techniques reported in literature inc...
Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be tra...
Both sampling and compression are performed simultaneously to reduce the number of measurements at the expense of increased computational cost for signal reconstruction. By combining CS with statistical learning, the number of required measurements can be further reduced, particularly if a given signal ...
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Image compression will be treated in a different chapter of this book. Here we only consider the problem of image quantization in the context of halftoning and color quantization. Generally, we formulate the problem as follows: Given an image f(n1, n2) of size N1 by N2, where each pixel ...