Spatially adaptive image denoising based on joint image statistics in the curvelet domain (original) (raw)
Related papers
Context adaptive image denoising through modeling of curvelet domain statistics
Journal of Electronic …, 2008
We perform a statistical analysis of curvelet coefficients, distinguishing between two classes of coefficients: those that contain a significant noise-free component, which we call the "signal of interest," and those that do not. By investigating the marginal statistics, we develop a prior model for curvelet coefficients. The analysis of the joint intra-and inter-band statistics enables us to develop an appropriate local spatial activity indicator for curvelets. Finally, based on our findings, we present a novel denoising method, inspired by a recent wavelet domain method called ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers.
2007
This paper proposes a new statistical model for curvelet coefficients of images to characterize both leptokurtic behavior and spatially clustering property of them. We employ a mixture of Gaussian probability density functions (pdfs) with local parameter to model the distribution of noise-free curvelet coefficients. This pdf is mixture and so it is able to model the heavy-tailed nature of curvelet coefficients. Since we use local parameters for mixture model, the proposed pdf can capture the clustering property of curvelet coefficients in spatial adjacent. This model is employed for noise reduction in a Bayesian framework using maximum a posteriori (MAP) estimator. We examine this method for denoising of several grayscale images such as CT image corrupted with additive Gaussian noise in various noise levels. The simulation results show that the proposed method has better performance visually and in terms of peek signal-to-noise ratio (PSNR) from several denoising methods in wavelet and curvelet domain.
Proc. ProR-ISC-Program …, 2006
In this paper, we perform an inter-sub-band statistical analysis of curvelet coefficients, making a distinction between two classes of coefficients: those representing useful image content and those dominated by noise. This analysis enables us to develop an appropriate inter-sub-band local spatial activity indicator (LSAI) for curvelets. We use this LSAI in our recently developed curvelet-based denoising method ProbShrinkCurv. The results demonstrate that the new method outperforms the waveletbased ProbShrink estimator as well as the existing curvelet-based methods, both for textured and for piecewise smooth images.
Image Denoising Based on Curvelet Transforms and its Comparative Study with Basic Filters
2013
Image denoising is basic work for image processing, analysis and computer vision. This Work proposes a Curvelet Transformation based image denoising, which is combined with the low pass filtering and thresholding methods in the transform domain. Through simulations with images contaminated by white Gaussian noise, this scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect as compared to basic filters. Curvelet transformation is a multi-scale transformation technique which is most suitable for the objects with curves. Introduction Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising ago...
A 4-quadrant Curvelet Transform for Denoising Digital Images
International Journal of Automation and Computing, 2013
The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.
Optik, 2018
Preservation of geometric components during image denoising using weighted bilateral filter and curvelet transforms is explored in this research. The proposed method emphases the texture and aritficats in an image while removing noise efficiently. Restoration of these details in an image not only improves the quality of image but also provides certain intelligence to the user for image understanding. Here, high frequency components are separated through weighted bilateral filter undergo curvelet transforms which leads to retaining of geometric features during the removal of noise components. Based on this, we propose a new method known as WBFCT and tested the performance in a simulated environment. Through a series of simulation of experiments we have compared the denoising performance of WBFCT with Standard Bilateral Filter (SBF), Robust Bilateral Filter (RBF), Weighted Bilateral Filter (WBF), LPG-PCA, KSVD, Curvelet only (Curvelet transform only without taking WBF), Wiener+Curvelet (Wiener filter in place of WBF), WBF+Wavelet (Wavelet transform in place of curvelet transform). Finally, the experimental outcomes divulged that present method has superior performance as compared to existing state-of-theart methods pertaining to Gaussian noise.
A wavelet-based image denoising technique using spatial priors
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2000
We propose a new wavelet-based method for image denoising that applies the Bayesian framework, using prior knowledge about the spatial clustering of the wavelet coefficients. Local spatial interactions of the wavelet coefficients are modeled by adopting a Markov Random Field model. An iterative updating technique known as iterated conditional modes (ICM) is applied to estimate the binary masks containing the positions of those wavelet coefficients that represent the useful signal in each subband. For each wavelet coefficient a shrinkage factor is determined, depending on its magnitude and on the local spatial neighbourhood in the estimated mask. We derive analytically a closed form expression for this shrinkage factor.
Wavelet-based image denoising using nonstationary stochastic geometrical image priors
Image and Video Communications and Processing 2003, 2003
In this paper a novel stochastic image model in the transform domain is presented and its superior performance in image denoising applications is demonstrated. The proposed model exploits local subband image statistics and is based on geometrical priors. Contrarily to complex models based on local correlations, or to mixture models, the proposed model performs a partition of the image into non-overlapping regions with distinctive statistics. A close form analytical solution of the image denoising problem for AWGN is derived and its performance bounds are analyzed. Despite being very simple, the proposed stochastic image model provides a number of advantages in comparison to the existing approaches: (a) simplicity of stochastic image modeling; (b) completeness of the model, taking into account multiresolution, non-stationary image behavior, geometrical priors and providing an excellent fit to the global image statistics; (c) very low complexity of the algorithm; (d) tractability of the model and of the obtained results due to the closed-form solution and to the existence of analytical performance bounds; (e) extensibility to different transform domains, such as orthogonal, biorthogonal and overcomplete data representations. The results of benchmarking with the state-of-the-art image denoising methods demonstrate the superior performance of the proposed approach.
Image Denoising Method based on Curvelet Transform with Thresholding Functions
Visual information which is transmitted in the form of digital images is becoming a major method of communication now a day. But the main drawback in digital images is the presence of noise while their acquisition or transmission. Removing noise from digital images is a challenge for researchers. Several noise removal algorithms have been proposed till date. Choice of any denoising algorithm is application dependent and it depends upon the type of noise present in the image. Every denoising method has its own assumptions, advantages and limitations. In this paper a new image denoising method which is based on Curvelet transform is proposed. The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier transform and wavelet transforms, in capturing the geometry of image edges are well known. Here we pursue "true" two dimensional transform, Curvelet Transform that can capture the intrinsic geometrical structure that is very important in visual information. Denoising of an image is done by Curvelet Transform with a thresholding function and the results are compared with different denoising methods. The Proposed method has the advantage of achieving a good visual quality of images while preserving the curved edges of an image. The proposed method is applied to different images such as grayscale image, color image, microscopic image, and seismic image. Experimental results show that proposed denoising technique performs better than other methods in terms of the PSNR.
PERFORMANCE ANALYSIS OF COLOR IMAGE DENOISING USING CURVELET TRANSFORM BASED TECHNIQUE
In this paper we propose a new method to reduce noise in color image. The images corrupted by Gaussian Noise is still a classical problem. To reduce the noise or to improve the quality of image we have used different parameters. The proposed method succeeded in providing improved image denoising performance to recover the shape of edges and important detailed components. The experimental results proved that the proposed technique can obtain a better image estimate than the curvelet transform based restoration methods.