Multiscale LMMSE based statistical estimation for image denoising

Image Denoising is the process of removal of the noise from the image contaminated by additive Gaussian noise without loss of features of image. It is a fundamental process in pattern recognition and image processing.In this thesis,a wavelet based new denoising scheme for estimation of parameters such as variance of the multiscale Linear minimum mean square error(LMMSE) estimator to derive optimal threshold using maximum a posterior (MAP) estimator of the noisy coefficients in wavelet domain has been proposed. Our proposed scheme modify the parameter of LMMSE. Input image is decomposed in four wavelet subband then for each subband the LMMSE estimator is then applied.Denoised image is reconstructed after applying inverse wavelet transform. Each schemes is studied separately and experiments are conducted on test images to evaluate the performance.This denoising scheme shows the best performance for highly corrupted image in terms of the structure similarity index measure(SSIM)the peak signal-to-noise ratio (PSNR)

SPECKLE NOISE REDUCTION USING MULTISCALE LMMSE (MLMMSE)-BASED FILTER

This report presents the project of studying the speckle noise reduction using Multiscale Least Minimum Mean Square Error (MLMMSE) filter. The MLMMSE filter is being compared in terms of feasibility, dependency and stability with the conventional image filter such as LEE 3X3, LEE 5X5, LEE 7X7 and Median filter. The estimation of the MLMMSE filter scheme for the image denoising is being proposed. Together with this project the wavelet selection to determine the best wavelet suit with MLMMSE filter is also being discussed. The principle of the speckle reduction is being used as the MLMMSE filtering are being perform with an undecimated domain wavelet. The image of the adaptive noise will be rescaling from the detail coefficient whereby the amplitude of the image signal will be divided with the variance ratio from the noisy image coefficient to the denoise image. This image is calculated analytically using the properties from the noisy image together with varying the variance and the selected optimal wavelet only. The original image is not resorting in order to obtain the result or to assessing the underlying backscattered signal. Experiment is carried out on normal image being test within two parameter that is Structural Similarity Index (SSIM) and Peak Signal Noise Ratio (PSNR) with varying the variance and the wavelet to identify the most suitable wavelet to run with MLMMSE filter for ultrasound images. The equivalent number of looks (ENL) is analysed in the last part of the experiment to demonstrate visual image quality is achieved for excellency in terms of the dependency of the images itself and also to avoid the typical of impairments of the images which normally created from the critically subsampled in the wavelet-based image denoising

Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI

Background: Prostate cancer is one of the most common forms of cancer found in males making early diagnosis important. Magnetic resonance imaging (MRI) has been useful in visualizing and localizing tumor candidates and with the use of endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The coils introduce intensity inhomogeneities and the surface coil intensity correction built into MRI scanners is used to reduce these inhomogeneities. However, the correction typically performed at the MRI scanner level leads to noise amplification and noise level variations. Methods: In this study, we introduce a new Monte Carlo-based noise compensation approach for coil intensity corrected endorectal MRI which allows for effective noise compensation and preservation of details within the prostate. The approach accounts for the ERC SNR profile via a spatially-adaptive noise model for correcting non-stationary noise variations. Such a method is useful particularly for improving the image quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available. Results: SNR and contrast-to-noise ratio (CNR) analysis in patient experiments demonstrate an average improvement of 11.7 dB and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong performance when compared to existing approaches. Conclusions: A new noise compensation method was developed for the purpose of improving the quality of coil intensity corrected endorectal MRI data performed at the MRI scanner level. We illustrate that promising noise compensation performance can be achieved for the proposed approach, which is particularly important for processing coil intensity corrected endorectal MRI data performed at the MRI scanner level and when the original raw data is not available.Comment: 23 page

Evaluation of Digital Speckle Filters for Ultrasound Images

Ultrasound (US) images are inherently corrupted by speckle noise causing inaccuracy of medical diagnosis using this technique. Hence, numerous despeckling filters are used to denoise US images. However most of the despeckling techniques cause blurring to the US images. In this work, four filters namely Lee, Wavelet Linear Minimum Mean Square Error (LMMSE), Speckle-reduction Anisotropic Diffusion (SRAD) and Non-local-means (NLM) filters are evaluated in terms of their ability in noise removal and capability to preserve the image contrast. This is done through calculating four performance metrics Peak Signal to Noise Ratio (PSNR), Ultrasound Despeckling Assessment Index (USDSAI), Normalized Variance and Mean Preservation. The experiments were conducted on three different types of images which is simulated noise images, computer generated image and real US images. The evaluation in terms of PSNR, USDSAI, Normalized Variance and Mean Preservation shows that NLM filter is the best filter in all scenarios considering both speckle noise suppression and image restoration however with quite slow processing time. It may not be the best option of filter if speed is the priority during the image processing. Wavelet LMMSE filter is the next best performing filter after NLM filter with faster speed

Effective SAR image despeckling based on bandlet and SRAD

Despeckling of a SAR image without losing features of the image is a daring task as it is intrinsically affected by multiplicative noise called speckle. This thesis proposes a novel technique to efficiently despeckle SAR images. Using an SRAD filter, a Bandlet transform based filter and a Guided filter, the speckle noise in SAR images is removed without losing the features in it. Here a SAR image input is given parallel to both SRAD and Bandlet transform based filters. The SRAD filter despeckles the SAR image and the despeckled output image is used as a reference image for the guided filter. In the Bandlet transform based despeckling scheme, the input SAR image is first decomposed using the bandlet transform. Then the coefficients obtained are thresholded using a soft thresholding rule. All coefficients other than the low-frequency ones are so adjusted. The generalized cross-validation (GCV) technique is employed here to find the most favorable threshold for each subband. The bandlet transform is able to extract edges and fine features in the image because it finds the direction where the function gives maximum value and in the same direction it builds extended orthogonal vectors. Simple soft thresholding using an optimum threshold despeckles the input SAR image. The guided filter with the help of a reference image removes the remaining speckle from the bandlet transform output. In terms of numerical and visual quality, the proposed filtering scheme surpasses the available despeckling schemes

SPECKLE NOISE REDUCTION USING MULTISCALE LMMSE (MLMMSE)-BASED FILTER

This report presents the project of studying the speckle noise reduction using Multiscale Least Minimum Mean Square Error (MLMMSE) filter. The MLMMSE filter is being compared in terms of feasibility, dependency and stability with the conventional image filter such as LEE 3X3, LEE 5X5, LEE 7X7 and Median filter. The estimation of the MLMMSE filter scheme for the image denoising is being proposed. Together with this project the wavelet selection to determine the best wavelet suit with MLMMSE filter is also being discussed. The principle of the speckle reduction is being used as the MLMMSE filtering are being perform with an undecimated domain wavelet. The image of the adaptive noise will be rescaling from the detail coefficient whereby the amplitude of the image signal will be divided with the variance ratio from the noisy image coefficient to the denoise image. This image is calculated analytically using the properties from the noisy image together with varying the variance and the selected optimal wavelet only. The original image is not resorting in order to obtain the result or to assessing the underlying backscattered signal. Experiment is carried out on normal image being test within two parameter that is Structural Similarity Index (SSIM) and Peak Signal Noise Ratio (PSNR) with varying the variance and the wavelet to identify the most suitable wavelet to run with MLMMSE filter for ultrasound images. The equivalent number of looks (ENL) is analysed in the last part of the experiment to demonstrate visual image quality is achieved for excellency in terms of the dependency of the images itself and also to avoid the typical of impairments of the images which normally created from the critically subsampled in the wavelet-based image denoising

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method

Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising

This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.Comment: 11 pages, 7 figur

Stationary Wavelet Transform for denoising Pulsed Thermography data: optimization of wavelet parameters for enhancing defects detection

Innovative denoising techniques based on Stationary Wavelet Transform (SWT) have started being applied to Pulsed Thermography (PT) sequences, showing marked potentialities in improving defect detection. In this contribution, a SWT-based denoising procedure is performed on high and low resolution PT sequences. Samples under test are two composite panels with known defects. The denoising procedure undergoes an optimization step. An innovative criterion for selecting the optimal decomposition level in multi-scale SWT-based denoising is proposed. The approach is based on a comparison, in the wavelet domain, of the information content in the thermal image with noise propagated. The optimal wavelet basis is selected according to two performance indexes, respectively based on the probability distribution of the information content of the denoised frame, and on the Energy-to-Shannon Entropy ratio. After the optimization step, denoising is applied on the whole thermal sequence. The approximation coefficients at the optimal level are moved to the frequency domain, then low-pass filtered. Linear Minimum Mean Square Error (LMMSE) is applied to detail coefficients at the optimal level. Finally, Pulsed Phase Thermography (PPT) is performed. The performance of the optimized denoising method in improving the defect detection capability respect to the non-denoised case is quantified using the Contrast Noise Ratio (CNR) criterion

Spatially-variant noise filtering in magnetic resonance imaging : a consensus-based approach

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a sub-sampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach