SPECKLE NOISE REDUCTION USING ADAPTIVE MULTISCALE PRODUCTS THRESHOLDING

Image denoising is an essential preprocessing technique in image acquisition systems. For instance, in ultrasound (US) images, suppression of speckle noise while preserving the edges is highly preferred. Thus, in this paper denoising the speckle noise by using wavelet-based multiscale product thresholding approach is presented. The underlying principle of this technique is to apply dyadic wavelet transform and performs the multiscale products of the wavelet transform. Then, an adaptive threshold is calculated and applied to the multiscale products instead of applying it on wavelet coefficient. Thereafter, the performance of the proposed technique is compared with other denoising techniques such as Lee filter, boxcar filter, linear minimum mean square error (LMMSE) filter and median filter. The result shows that the proposed technique gives a better performance in terms of PNSR and ENL value by an average gain of 1.22 and 1.8 times the noisy on, respectively and can better preserved image detail

IMAGE DENOISING USING TRADITIONAL WAVELET THRESHOLDING

ABSTRACT Edge-preserving denoising is of great interest in image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of the images. A dyadic wavelet transform (A Canny edge detector-) is also employed. An adaptive scale correlation wavelet thresholding technique is then proposed. In which the adaptive threshold will be calculated which is imposed on the products, instead of on the wavelet coefficients. This proposed scheme suppresses the noise effectively and preserves the edges features than other wavelet-thresholding denoising methods. In the result we can see the better visual quality and increment in the signal to noise the last node will die in the network is to be discussed

A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation

In this article we derive an unbiased expression for the expected mean-squared error associated with continuously differentiable estimators of the noncentrality parameter of a chi-square random variable. We then consider the task of denoising squared-magnitude magnetic resonance image data, which are well modeled as independent noncentral chi-square random variables on two degrees of freedom. We consider two broad classes of linearly parameterized shrinkage estimators that can be optimized using our risk estimate, one in the general context of undecimated filterbank transforms, and another in the specific case of the unnormalized Haar wavelet transform. The resultant algorithms are computationally tractable and improve upon state-of-the-art methods for both simulated and actual magnetic resonance image data.Comment: 30 double-spaced pages, 11 figures; submitted for publicatio

A new approach to automated retinal vessel segmentation using multiscale analysis

Author name used in this publication: David ZhangRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A New Hybridization of Bilateral and Wavelet Filters for Noisy De-Noisy Images

In this work we propose, a hybrid noise reduction algorithm that is a combination of a spatial field binary filter and a hybrid wave field threshold function. These two methods are used to stop Gaussian noise. The hybrid filter is a nonlinear filter that deals with spatial averaging of non-uniform edges. We found it to be an effective technique for image reduction. Determining filter parameters for the mixed filter is important to avoid large differences in results, besides the issue of acceleration velocity. This hybrid model, binary filtering, and Wavelet Thresholding have tried standard images, such as normal eyes, MRI, Roya Face, Ultrasound, X-Ray, and Rawa. Different Gaussian noise was added with different standard deviations σ = 10, 20, 35, 40, and 50. The peak-to-noise ratio (PSNR) signal, MSE, VIF, IQI, and the proposed model MSE between pixels were used as quantitative measures of performance of the relative noise reduction algorithms and then were compared to the models

Advancement in Denoising MRI Images via 3D-GAN Model with Direction Coupled Magnitude Histogram Consistency Loss

The diagnostics of medical pictures are essential for recognizing and comprehending a wide range of medical problems. This work introduces the Direction Coupled Magnitude Histogram (DCMH) as a novel structure picture descriptor to improve diagnostic accuracy. One of DCMH's unique selling points is its ability to include the edge oriented information that are oriented in any way inside a frame, enabling the expression of delicate nuances using various gradient features. The proposed method applies cartoon texture based textural loss and DCMH based structural loss to identify and analyse structural and textural information during the denoising time. A major contribution that improves the interpretability of images by emphasizing structural aspects that is inherent to the image. The proposed DCMH_3D_GANaverage results show exceptional performance, with an SSIM of 0.972995 and PSNR of 48.74, highlighting the effectiveness of the DCMH-based method in enhancing medical picture diagnosis. The capacity of Structured Loss to improve picture interpretability and lead to a more precise diagnosis is unquestionably advantageous. The newly developed DCMH-based approach, which includes texture loss and structured components, is a promising development in healthcare image processing that will enable better patient care through enhanced diagnostic abilities