A skewness reformed complex diffusion based unsharp masking for the restoration and enhancement of Poisson noise corrupted mammograms

2022 ◽  
Vol 73 ◽  
pp. 103421
Author(s):  
Abhinav Kumar ◽  
Pradeep Kumar ◽  
Subodh Srivastava
Keyword(s):  
Poisson Noise ◽  
Unsharp Masking ◽  
Complex Diffusion

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking

2021 ◽  
pp. 095441192110393 ◽  
Author(s):  
Abhinav Kumar ◽  
Subodh Srivastava
Keyword(s):  
Breast Cancer ◽  
Performance Evaluation ◽  
Quantitative Evaluation ◽  
Mean Squared Error ◽  
Imaging Modality ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Unsharp Masking ◽  
Complex Diffusion ◽  
Extended Complex

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.

Enhancement and Restoration of Microscopic Images Corrupted with Poisson's Noise Using a Nonlinear Partial Differential Equation-based Filter

2011 ◽  
Vol 61 (5) ◽  
pp. 452 ◽  
Author(s):  
Rajeev Srivastava ◽  
JRP Gupta ◽  
Harish Parthasarathy
Keyword(s):  
Signal To Noise Ratio ◽  
Nonlinear Partial Differential Equation ◽  
Wiener Filter ◽  
Similarity Index ◽  
Energy Functional ◽  
Poisson Noise ◽  
Prior Model ◽  
Microscopic Images ◽  
Complex Diffusion ◽  
Image Prior

<p>An inherent characteristic of the many imaging modalities such as fluorescence microscopy and other microscopic modalities is the presence of intrinsic Poisson noise that may lead to degradation of the captured image during its formation. A nonlinear complex diffusion-based filter adapted to Poisson noise is proposed in this paper to restore and enhance the degraded microscopic images captured by imaging devices having photon limited light detectors. The proposed filter is based on a maximum a posterior approach to the image reconstruction problem. The formulation of the filtering problem as maximisation of a posterior is useful because it allows one to incorporate the Poisson likelihood term as a data attachment which can be added to an image prior model. Here, the Gibb's image prior model-based on energy functional defined in terms of gradient norm of the image is used. The performance of the proposed scheme has been compared with other standard techniques available in literature such as Wiener filter, regularised filter, Lucy-Richardson filter and another proposed nonlinear anisotropic diffusion-based filter in terms of mean square error, peak signal-to-noise ratio, correlation parameter and mean structure similarity index map.The results shows that the proposed complex diffusion-based filter adapted to Poisson noise performs better in comparison to other filters and is better choice for reduction of intrinsic Poisson noise from the digital microscopic images and it is also well capable of preserving edges and radiometric information such as luminance and contrast of the restored image.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.452-461</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1181</strong></strong></p>

Detection of a Pairwise-Correlated Stream of Signals Against a Poisson Noise Background

1999 ◽  
Vol 53 (7-8) ◽  
pp. 69-78
Author(s):  
A. I. Strelkov ◽  
O. M. Stadnyk ◽  
S. I. Kalmykov ◽  
A. P. Lytyuga
Keyword(s):  
Poisson Noise ◽  
Noise Background

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

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