Image denoising using wavelet transform and wiener filter based on log energy distribution over Poisson-Gaussian noise model

Boyat, Ajay; Joshi, Brijendra Kumar

doi:10.1109/iccic.2014.7238350

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…Boyat and Joshi [18] practiced image denoising using the Poisson Gaussian noise model. They also employed the integration of the Wiener filter in a wavelet domain depending on the log energy distribution method to the denoised image corrupted by Poisson Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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Nowadays, a digital image is often easily corrupted due to different forms of noise and complex processes resulting from the acquisition, compression, encoding, transportation, storage, retrieval, etc. All of these factors cause image quality to be distorted and visual information to be lost; in order to overcome this problem, Image denoising techniques are used widely to eliminate the various forms of noise that exist in the deteriorating image while keeping as many fine details and vital signal features as possible in the digital image. The wavelet denoising method aims to remove unwanted noise from a noisy image while preserving its vital features as a result of its ability to divide the degraded image into four sub-bands (sub-images) and operate at the frequencies of each one separately, where acquiring the original image content is vital to achieving reliable performance. This work introduces and implements a new hybrid system to the image denoising caused by Additive White Gaussian Noise (AWGN). The hybrid system is achieved using a combination of Median and Wiener filters as spatial domain filters with two-dimensional stationary and discrete wavelet transform (2D-SWT, 2D-DWT) as a multi-resolution analysis technique by applying 131 wfilters from the wavelet families (haar, db, sym, coif, bior, rbio, dmey, fk) in image processing at three levels of decomposition based on Hard, SureShrink, Bayesian, and Penalized threshold techniques on both high and low frequencies to distinguish and remove noise from affected pixel units and obtain improved results of the noise reduction process to the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) eliminates noise and completes the image reconstruction by the hybrid denoising technique. Finally, the performance of the hybrid system has been estimated and measured by the peak signal-to-noise ratio (PSNR) value as an image quality metric. Experimental evaluation findings that the results of the proposed approach improved by about 17.5% by comparing them to the results of the related work, as well as enhancement the essence of image quality in terms of better noise reduction and edge preservation instead of using a multi-resolution WT domain or spatial domain filters separately.

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Section: Related Workmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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“…The paper introduces the two most fantastic noise models, jointly called as Poisson-Gaussian noise model. These two noise models are specified the quality of MRI recipient signal in terms of visual appearances and strength [21]. Despite from the highest quality MRI processing, above model describes the set of parameters of the Poisson-Gaussian noise corrupted test image.…”

Section: Poisson-gaussian Noisementioning

confidence: 99%

A Review Paper : Noise Models in Digital Image Processing

Boyat¹,

Joshi²

2015

SIPIJ

Self Cite

278

115

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“…More sophisticated methods are often formulated as iterative algorithms and variational models, with a variety of data fidelity and regularization terms being proposed in literature. The most common approaches are (regularized) inverse filtering, including, e.g., Wiener filtering [ 8 , 9 ] and (regularized) Lucy–Richardson (LR) deconvolution [ 10 , 11 ]. For an overview, please refer to, e.g., [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data

Woelk

Kannabiran

Brock

et al. 2021

IJMS

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Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

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Image denoising using wavelet transform and wiener filter based on log energy distribution over Poisson-Gaussian noise model

Cited by 13 publications

References 23 publications

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

A Review Paper : Noise Models in Digital Image Processing

Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data

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