Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function (original) (raw)

Combination of Spatial Filtering and Adaptive Wavelet Thresholding for Image Denoising

International Journal of Image, Graphics and Signal Processing

Thresholding in wavelet domain has proven very high performances in image denoising and particularly for homogeneous ones. Conversely, and in cases of relatively non-homogeneous scenes, it often induces the loss of some true coefficients; inducing so, to smoothing the details and the different features of the thresholded image. Therefore, and in order to overcome this shortcoming, we introduce within this paper a new alternative made by a combination of advantages of both spatial filtering and wavelet thresholding; that ensures well removing the noise effect while preserving the different features of the considered image. First, the degraded image is decomposed into wavelet coefficients via a 2-level 2D-DWT. Then, the finest detail sub-bands likely due to noise, are thresholded in order to maximally cancel the noise contribution. The remaining noise shared across the coarse detail subbands (LH2, HL2, and HH2) is cleaned by filtering these mentioned sub-bands via an adaptive wiener filter instead of thresholding them; avoiding so smoothing the acquired image. Finally, a joint bilateral filter (JBF) is applied to ensure the preservation of the different image features. Experimental results show notable performances of our new proposed scheme compared to the recent state-of-the-art schemes visually and in terms of (MSE), (PSNR) and correlation coefficient.

An Efficient Denoising Model based on Wavelet and Bilateral Filters

International Journal of Computer Applications, 2012

This paper investigates different models developed through hybridization of wavelet and bilateral filters for denoising of variety of noisy images. Hybridization between wavelet thresholding and bilateral filter is done in different configurations. The models are experimented on standard images like Lena, Barbara, Einstein and satellite as well as astronomical telescopic images and their performances are evaluated in terms of peak signal to noise ratio (PSNR) and image quality index (IQI). Out of number of trial models developed, only 25 models are reported as the performance of the rest models are too poor to be reported. Results demonstrate that use of bilateral filters in combination with wavelet thresholding filters in different ways on decomposed subbands deteriorates the performance. But the application of bilateral filter before or after or both before and after decomposition enhances the performance. Specifically, the filter developed with bilateral filter before decomposition of an image is found to give uniform and consistent results on all the images.

An Improved Image Denoising Method Based on Wavelet Thresholding

Journal of Signal and Information Processing, 2012

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

Image denoising based on gaussian/bilateral filter and its method noise thresholding

Signal, Image and Video Processing, 2012

The Gaussian filter is a local and linear filter that smoothes the whole image irrespective of its edges or details, whereas the bilateral filter is also a local but non-linear, considers both gray level similarities and geometric closeness of the neighboring pixels without smoothing edges. The extension of bilateral filter: multi-resolution bilateral filter, where bilateral filter is applied to approximation subbands of an image decomposed and after each level of wavelet reconstruction. The application of bilateral filter on the approximation subband results in loss of some image details, whereas that after each level of wavelet reconstruction flattens the gray levels thereby resulting in a cartoon-like appearance. To tackle these issues, it is proposed to use the blend of Gaussian/bilateral filter and its method noise thresholding using wavelets. In Gaussian noise scenarios, the performance of proposed methods is compared with existing denoising methods and found that, it has inferior performance compared to Bayesian least squares estimate using Gaussian Scale mixture and superior/comparable performance to that of wavelet thresholding, bilateral filter, multi-resolution bilateral filter, NL-means and Kernel based methods. Further, proposed methods have the advantage of less computational time compared to other methods except wavelet thresholding, bilateral filter.

A Comparative Study of Wavelet Thresholding for Image Denoising

Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three stepsfirst computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.

A Review on Image Denoising based on Wavelet Transform for different noises

Research Trend, 2017

Due to some technical and environmental problem image get corrupted by different type of noises such as salt and pepper, Gaussian, Poisson or speckle noise during transmission and acquisition. In modern day, Wavelet transform method is used to denoised image which first of all cut up data into different frequency component. There are several advantages of Wavelet transform as compared to other techniques such as wavelet transform has best localization properties. In this paper we have compared different thresholding techniques such as Global threshold, Visu Shrink and Bayes Shrink which is based on the wavelet transform for image denoising. We have also calculated the PSNR and RMSE value for denoised images.

A Hybrid Image Denoising Technique based on FNLM and Wavelet Thresholding

International Journal of Innovative Research in Computer and Communication Engineering, 2015

Image denoising constitutes the first step in image processing domain. In order to solve this frequently occurring problem various methods have been proposed. In this paper a denoising algorithm is proposed which uses the combination of Fast Non-Local means filter (FNLM) and adaptive wavelet thresholding. The performance of FNLM filter deteriorates with the increase of noise amount in the image. This hybrid approach solves this problem. Firstly, the image is denoised by the FNLM filter and then its method noise is obtained. The method noise is subjected to adaptive wavelet thresholding for extracting the image details that were removed by the FNLM filter. These extracted details are added to the output of FNLM filter and a noise free image is recovered with all the edges and image details preserved.

A New Perspective of Wavelet Based Image Denoising Using Different Wavelet Thresholding

International Journal of Science and Research (IJSR), 2016

In this paper, the basic principles of digital image processing and image denoising algorithms are summarized. Till now digital image processing has very large scope almost all of the technical domain i.e. image enhancement, image compression, image synthesis, image restoration, image denoising and image analysis. Present paper focuses in the domain of Image denoising. Image denoising is a process of removing noise without affecting and distorting the image and produce a better quality of denoised image. In the present paper, authors observes the effect of various wavelet thresholding methods such as Visushrink, Bayesshrink, Sureshrink and compare all of these with respect to denoised image and introduce a proposed denoising algorithm to determine that it gives better result compare to existing methods.

An Efficient Implementation of Neighborhood based Wavelet Thresholding For Image Denoising

In this paper we propose computationally efficient denoising algorithm that thresholds the wavelet coefficient considering its neighbors in deciding whether it is noisy or noise free. The proposed algorithm select a suboptimal threshold and neighboring window size for every subband that minimized Mean Square Error(MSE) in the denoised image using Stein's Unbiased Risk Estimate(SURE). In this paper, we demonstrate the efficiency of the proposed denoising algorithm as compared with two other state-of-the art denoising algorithms.

A Hybrid Image Denoising Technique Using Neighbouring Wavelet Coefficients

Frontiers in Signal Processing

This paper proposes a hybrid image denoising technique using neighbouring wavelet coefficients. The NeighShrink method groups the wavelet coefficients in non overlapping blocks and then thresholds empirically them blockwise. This method does not give good quality of image since it removes too many small wavelet coefficients. Our proposed scheme retains the modified coefficients and also gives good performance in terms of peak signal-to-noise ratio.