A Novel Method of Image Denoising: New Variant of Block Matching and 3D

2020 ◽  
Vol 10 (10) ◽  
pp. 2490-2500
Author(s):  
Sadaf Zahid Mahmood ◽  
Humaira Afzal ◽  
Muhammad Rafiq Mufti ◽  
Nadeem Akhtar ◽  
Asad Habib ◽  
...  
Keyword(s):  
Image Denoising ◽  
Adaptive Filter ◽  
Distance Measure ◽  
Signal To Noise Ratio ◽  
Visual Image ◽  
Block Matching ◽  
Experimental Result ◽  
New Variant ◽  
Novel Method ◽  
Texture Preservation

The demand of accurate and visually fair images is increasing with the passage of time and bang of the number of digital images especially in the domain of medical and healthcare systems. The visual image quality of modern cameras affected due to edges, textures and sharp structures noise. Though research community has introduced several techniques such as BM3D (Block Matching and 3D) for image denoising. However, edges and texture preservation capabilities remain issues due to hard thresholds values and captured image diversity. In order to address these issues, we propose a new variant of BM3D namely BM3DMA (Block Matching and 3D with Mahalanobis and Adaptive filter) which is employed through the use of Mahalanobis distance measure (for diversity coverage) and adaptive filter (for soft thresholds). We used two widely known datasets consist of set of standard and medical images. We observe 5% to 10% enhancement in the performance of BM3DMA as compared to BM3D in terms of improving the PSNR (Peak Signal to Noise Ratio) value. The promising experimental result indicates the effectiveness of BM3DMA in terms preserving the edge and texture image noise.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

scholarly journals An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shengnan Zhang ◽  
Lei Wang ◽  
Chunhong Chang ◽  
Cong Liu ◽  
Longbo Zhang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Evaluation Criteria ◽  
Structural Similarity ◽  
Block Matching ◽  
Generative Adversarial Network ◽  
Denoising Method ◽  
Edge Preserving ◽  
Block Matching Algorithm ◽  
Adversarial Network

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.

scholarly journals Edge-aware image denoising algorithm

2018 ◽  
Vol 13 ◽  
pp. 174830181880477
Author(s):  
Xiangning Zhang ◽  
Yan Yang ◽  
Lening Lin
Keyword(s):  
Image Denoising ◽  
Information Needs ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Block Matching ◽  
External Information ◽  
Noisy Image ◽  
Original Image ◽  
Similar Images ◽  
Edge Features

The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches. In this paper, an edge-aware image denoising algorithm is proposed to achieve the goal of preserving the details of original image while denoising and using only the characteristics of the noisy image. In general, image denoising algorithms use the noise prior to set parameters todenoise the noisy image. In this paper, it is found that the details of original image can be better preserved by combining the prior information of noise and the image edge features to set denoising parameters. The experimental results show that the proposed edge-aware image denoising algorithm can effectively improve the performance of block-matching and 3D filtering and patch group prior-based denoising algorithms and obtain higher peak signal-to-noise ratio and structural similarity values.

An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

2021 ◽  
Vol 11 (3) ◽  
pp. 1-20
Author(s):  
Nandini H. M. ◽  
Chethan H. K. ◽  
Rashmi B. S.
Keyword(s):  
Distance Measure ◽  
Video Retrieval ◽  
Experimental Result ◽  
Gradual Transition ◽  
Mean Deviation ◽  
Global Features ◽  
Video Shot ◽  
Subsequent Step ◽  
Block Based ◽  
Binary Weight

Shot boundary detection in videos is one of the most fundamental tasks towards content-based video retrieval and analysis. In this aspect, an efficient approach to detect abrupt and gradual transition in videos is presented. The proposed method detects the shot boundaries in videos by extracting block-based mean probability binary weight (MPBW) histogram from the normalized Kirsch magnitude frames as an amalgamation of local and global features. Abrupt transitions in videos are detected by utilizing the distance measure between consecutive MPBW histograms and employing an adaptive threshold. In the subsequent step, co-efficient of mean deviation and variance statistical measure is applied on MPBW histograms to detect gradual transitions in the video. Experiments were conducted on TRECVID 2001 and 2007 datasets to analyse and validate the proposed method. Experimental result shows significant improvement of the proposed SBD approach over some of the state-of-the-art algorithms in terms of recall, precision, and F1-score.

Disparity Surface Reconstruction Based on a Stereo Light Microscope and Laser Fringes

2018 ◽  
Vol 24 (5) ◽  
pp. 503-516
Author(s):  
Yuezong Wang
Keyword(s):  
Surface Reconstruction ◽  
Light Microscope ◽  
Stereo Matching ◽  
Block Matching ◽  
Stereo Image ◽  
Reference Plane ◽  
Motion Direction ◽  
Measuring Techniques ◽  
Novel Method ◽  
Disparity Contours

AbstractMicroscopic vision systems based on a stereo light microscope (SLM) are used in microscopic measuring fields. Conventional measuring methods output the disparity surface based on stereo matching methods; however, these methods require that stereo images contain sufficient distinguishing features. Moreover, matching results typically contain many mismatched points. This paper presents a novel method for disparity surface reconstruction by combining an SLM and laser measuring techniques. The surfaces of small objects are scanned by a laser fringe, and a stereo image sequence containing laser stripes is obtained. The central contours of the laser stripes are extracted, and central contours are derived for alignment. A disparity coordinate system is then defined and used to analyze the relationship between the motion direction and reference plane. Next, the method of aligning disparity contours is proposed. The results show that our method can achieve a precision of ±0.5 pixels and that the real and measured shapes described by the disparity surface are consistent based on our method. Our method is confirmed to perform much better than the conventional block-matching method. The disparity surface output obtained by our method can be used to measure the surface profiles of microscopic objects accurately.

A Robust Procedure to Implement Dynamic Stall Models Into Actuator Line Methods for the Simulation of Vertical-Axis Wind Turbines

2021 ◽  
Author(s):  
Pier Francesco Melani ◽  
Francesco Balduzzi ◽  
Alessandro Bianchini
Keyword(s):  
Flow Field ◽  
Wind Turbines ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Vertical Axis ◽  
Dynamic Stall ◽  
Lumped Parameter ◽  
Low Pass ◽  
Unsteady Rans ◽  
Novel Method

Abstract The Actuator Line Method (ALM), combining a lumped-parameter representation of the rotating blades with the CFD resolution of the turbine flow field, stands out among the modern simulation methods for wind turbines as probably the most interesting compromise between accuracy and computational cost. Being however a method relying on tabulated coefficients for modeling the blade-flow interaction, the correct implementation of the sub-models to account for higher order aerodynamic effects is pivotal. Inter alia, the introduction of a dynamic stall model is extremely challenging: first, it is important to extrapolate a correct value of the angle of attack (AoA) from the solved flow field; second, the AoA history needed to calculate the rate of dynamic variation of the angle itself is characterized by a low signal-to-noise ratio, leading to severe numerical oscillations of the solution. The study introduces a robust procedure to improve the quality of the AoA signal extracted from an ALM simulation. It combines a novel method for sampling the inflow velocity from the numerical flow field with a low-pass filtering of the corresponding AoA signal based on Cubic Spline Smoothing. Such procedure has been implemented in the Actuator Line module developed by the authors for the commercial ANSYS® FLUENT® solver. To verify the reliability of the methodology, two-dimensional unsteady RANS simulations of a test 2-blade Darrieus H-rotor, for which high-fidelity experimental and numerical blade loading data were available, have been performed for a selected unstable operation point.

A Novel Method with Adaptive Non-local Means for Image Denoising

2012 ◽  
Vol 4 (16) ◽  
pp. 251-257
Author(s):  
Wenhui Li ◽  
Bo Fu ◽  
Chunri Cui ◽  
Huiying Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Local Means ◽  
Novel Method ◽  
Non Local
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