Image De-Speckling Based on the Coefficient of Variation, Improved Guided Filter, and Fast Bilateral Filter

2021 ◽  
pp. 2250036
Author(s):  
Hadi Salehi
Keyword(s):  
Coefficient Of Variation ◽  
Wiener Filter ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Input Image ◽  
Guided Filter ◽  
Ultrasound Images ◽  
Sar Images ◽  
Degraded Images ◽  
Image Detail

Images are widely used in engineering. Unfortunately, medical ultrasound images and synthetic aperture radar (SAR) images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, first, an optimized adaptive Wiener filter (OAWF) is proposed. OAWF can be applied to the input image without the need for logarithmic transform. In addition its performance is improved. Next, the coefficient of variation (CV) is computed from the input image. With the help of CV, the guided filter converts to an improved guided filter (IGF). Next, the improved guided filter is applied on the image. Subsequently, the fast bilateral filter is applied on the image. The proposed filter has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.

A Novel Hybrid Filter for Image Despeckling Based On Improved Adaptive Wiener Filter, Bilateral Filter and Wavelet Filter

2021 ◽  
pp. 2150036
Author(s):  
Hadi Salehi ◽  
Javad Vahidi
Keyword(s):  
Wiener Filter ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Fuzzy Classification ◽  
Second Phase ◽  
Ultrasound Images ◽  
Wavelet Filter ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Three Phases

Images are widely used in engineering. But, some images such as medical ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, we suggest three phases and three denoising filters. In the first phase, the coefficient of variation is computed from the noisy image. Next, fuzzy c-means (FCM) is applied to the coefficients of variation. Applying FCM leads to the fuzzy classification of image regions. Next, the second phase is a hybrid of the three denoising filters. Fast bilateral filter (BF) for homogeneous regions, improved the adaptive wiener filters (AWFs) and wavelet filter that are applied on homogeneous, detail and edge regions, respectively. The proposed improved AWF has been developed from the AWF. In the third phase, the output image is evaluated by the fuzzy logic approach. Thus, with three phases, the proposed method has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.

scholarly journals Denoising of Medical Ultrasound Images using Wavelet Transform with Bilateral Filter

2019 ◽  
Vol 8 (4) ◽  
pp. 8113-8116
Keyword(s):  
Wavelet Transform ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Speckle Noise ◽  
Computer Assisted ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Edge Preservation ◽  
Neighborhood Relationship ◽  
Speckle Filtering

Medical image degradation contains a significant impact on image quality and therefore affects the human interpretation and also the accuracy of computer assisted diagnostics techniques, unfortunately ultrasound images are principally degraded by an intrinsic noise known as speckle noise. Therefore, de- speckle filtering may be pre-processing step in medical ultrasound images. During this paper we propose a new image de-noising technique is the combination of bilateral filter and wavelet transform. The main contribution of this paper is within the use of a new neighborhood relationship to develop a new multi-scale bilateral filter. Experimental outcomes validate the usefulness and also the correctness of the proposed filter in edge preservation and speckle noise reduction for medical ultrasound images.

scholarly journals A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

2020 ◽  
Vol 12 (15) ◽  
pp. 2371 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Thabet Abdeljawad ◽  
Aziz Khan ◽  
Seyed Yaser Bozorgi Rad
Keyword(s):  
Additive Noise ◽  
Weighted Least Squares ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Input Image ◽  
Logarithmic Transformation ◽  
Guided Filter ◽  
Sar Image ◽  
Edge Information ◽  
Image Despeckling

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter

2020 ◽  
Vol 20 (03) ◽  
pp. 2050020
Author(s):  
Hadi Salehi ◽  
Javad Vahidi
Keyword(s):  
Ultrasound Image ◽  
Bilateral Filter ◽  
Intrinsic Noise ◽  
Second Phase ◽  
Ultrasound Images ◽  
Third Phase ◽  
Coefficients Of Variation ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Homogeneous Regions

Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a proposed adaptive bilateral filter (ABF). The ABF helps for better denoising based on the three regions which are edge, detail and homogeneous regions. The next step, a weight, is applied to the ABF. This step is for isolated noise denoising. Next, in the third phase, the output image is evaluated by the fuzzy logic approach. The proposed method is compared with other filters in the literature. The experimental outcomes show that the proposed method has better performance than the other filters. That proposed denoising algorithm is able to preserve image details and edges when compared with other denoising methods.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

Color image detail enhancement based on quaternion guided filter

2017 ◽  
Vol 24 (4) ◽  
pp. 40-50 ◽  
Author(s):  
Wu Kun ◽  
Li Guiju ◽  
Han Guangliang ◽  
Yang Hang ◽  
Liu Peixun
Keyword(s):  
Color Image ◽  
Guided Filter ◽  
Image Detail ◽  
Detail Enhancement

A Novel Despeckling Method for Medical Ultrasound Images Based on the Nonsubsampled Shearlet and Guided Filter

2019 ◽  
Vol 39 (3) ◽  
pp. 1449-1470 ◽  
Author(s):  
Ju Zhang ◽  
Xiaojie Xiu ◽  
Jun Zhou ◽  
Kailun Zhao ◽  
Zheng Tian ◽  
...  
Keyword(s):  
Guided Filter ◽  
Ultrasound Images ◽  
Medical Ultrasound
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