This paper deals with a restoration (both denoising and deblurring) method. For instance in the case of denoising, this latter is only a small modification from the usual wavelet thresholding. However, it has the sign...
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ISBN:
(纸本)0819441929
This paper deals with a restoration (both denoising and deblurring) method. For instance in the case of denoising, this latter is only a small modification from the usual wavelet thresholding. However, it has the significant advantage to allow the use of several bases in such a way that we select what is considered as information by a basis or another basis or another basis, and so on for as many bases as we want. The computational cost of the method is mainly the computation of the coordinates of the signal (or image) in the bases.
The problem of image denoising has received more attention than the problem of image sharpening. In the paper, we propose that wavelet-based algorithms for image denoising can be used to perform image sharpening. Cons...
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ISBN:
(纸本)0819450804
The problem of image denoising has received more attention than the problem of image sharpening. In the paper, we propose that wavelet-based algorithms for image denoising can be used to perform image sharpening. Consequently, a variety of new image sharpening techniques becomes available. We examine the sharpening of natural images using an algorithm for image denoising with oriented complex 2D wavelets.
Due to the imperfect physical arrangement of camera sensors, spectral bands of ground observation satellite images are usually shifted relative to each other. In order to address this issue, we propose a computational...
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ISBN:
(纸本)9781538615010
Due to the imperfect physical arrangement of camera sensors, spectral bands of ground observation satellite images are usually shifted relative to each other. In order to address this issue, we propose a computationally simple band registration method which is based on Dynamic Time Warping (DTW) and Discrete wavelet Transform (DWT) algorithms. This method has been tested on 10 frames of GOKTURK-2 images and compared to a Scale-Invariant Feature Transform (SIFT) based method. In terms of quality, the proposed method have yielded very close results compared to SIFT.
The general subband decomposition problem is discussed. A fast and optimally localized biorthogonal wavelet transformation, suitable for image compression applications, is proposed. The performance of this transformat...
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We present in the following work, a multiscale edge detection algorithm whose aim is to detect edges of any slope. Our work is based on a generalization of the Canny-Deriche filter, modelized by a more realistic edge ...
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ISBN:
(纸本)0819416274
We present in the following work, a multiscale edge detection algorithm whose aim is to detect edges of any slope. Our work is based on a generalization of the Canny-Deriche filter, modelized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a frame of wavelets. For the merging of the wavelet coefficients, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up and after the training phase does not require any adjustment nor parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting.
As a generalization of the concept of pseudo-biorthogonal bases (PBOB), we already presented the theory of the so-called extended pseudo-biorthogonal bases (EPBOB). We introduce in this paper two special types of EPBO...
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ISBN:
(纸本)0819416274;9780819416278
As a generalization of the concept of pseudo-biorthogonal bases (PBOB), we already presented the theory of the so-called extended pseudo-biorthogonal bases (EPBOB). We introduce in this paper two special types of EPBOB called EPBOB's of Type O and of type L. Characterizations, construction methods, inherent properties, and mutual relations of these types of EPBOB are discussed.
image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian base...
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ISBN:
(纸本)9781479902699;9781479902675
image denoising is an active area of research and probably one of the most studied problems in the imageprocessing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian based neighborhood spatial filter with wavelet transform that based on neighborhood thresholding function which takes the correlation of the magnitude of the wavelet coefficient with its neighbors into consideration to decide whether the coefficient is noisy or noise free. Accordingly, noises are detected with the help of the surrounding information and are removed. Experimental results show that the proposed algorithm can effectively remove the image noises with less processing time as compared with the state-of-the-art denoising algorithm.
The basic objective of this paper is to analyze the concept of wavelet based algorithms for image compression using different parameter. All algorithms are based on still images, The algorithm involved in the comparat...
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ISBN:
(纸本)9781479933587
The basic objective of this paper is to analyze the concept of wavelet based algorithms for image compression using different parameter. All algorithms are based on still images, The algorithm involved in the comparative analysis is wavelet Difference Reduction (WDR), Spatial orientation tree wavelet (STW), Embedded zero tree wavelet (EZW) and modified Set Partitioning in hierarchical trees (SPIHT). These algorithms are more effective and deliver a better feature in the image. In compression, wavelets transform have shown a good elasticity to a large amount of data, while being of realistic complexity. These techniques are used in many imageprocessingapplications. The techniques are compared by using the performance parameters peak signal to noise ratio (PSNR) & mean square error (MSE).
Noise is one of the most widespread problems present in nearly all imaging applications. The search for efficient image denoising methods is still a valid challenge. In spite of the sophistication of the recently prop...
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ISBN:
(纸本)9781467358057
Noise is one of the most widespread problems present in nearly all imaging applications. The search for efficient image denoising methods is still a valid challenge. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. They all show an outstanding performance when the image model corresponds to the algorithm assumptions, but fail in general and create artifacts or remove fine image structures. Therefore, a universal "best" filter has yet to be found. wavelet analysis is a new method consisting of a set basis functions that can be used to analyze signals in both time (or space) and frequency domains simultaneously. In this paper, a novel hybrid filter for image despeckling that combines wavelet denoising and an enhanced adaptive Kuan filter is proposed, resulting in a significant gain with respect to many spatial as well as wavelet-based speckle reduction filters.
In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) filter is derived for the most general case. signal-dependent n...
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ISBN:
(纸本)0819437646
In this paper, after reviewing a general model to deal with signal-dependent image noise, the well known Local Linear Minimum Mean Squared Error (LLMMSE) filter is derived for the most general case. signal-dependent noise filtering is approached in a multiresolution framework either by LLMMSE processing ratios of combinations of lowpass images, which are tailored to the noise model in order to mitigate its signal-dependence, or by thresholding a normalized nonredundant wavelet transform designed to yield signal-independent noisy coefficients as well. Experimental results demonstrate that the Laplacian pyramid approach largely outperform LLMMSE filtering on a unique scale and is still superior to wavelet denoising by soft-thresholding.
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