Image Noise Removal Method Based on Thresholding and Regularization Techniques

Hiền, Nguyễn Ngọc; Thanh, Dang N. H.; Erkan, Uğur; Tavares, João Manuel R. S.

doi:10.1109/access.2022.3188315

Cited by 13 publications

(7 citation statements)

References 41 publications

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“…In this article, the peak signal‐to‐noise ratio (PSNR) and three histograms are selected as objective indicators to measure the coloring effect. PSNR is a traditional image evaluation method that is often used for quality evaluation after image denoising 32 …”

Section: Methodsmentioning

confidence: 99%

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Real‐time coloring method of laser surgery video based on generative adversarial network

Pan¹,

Lian

et al. 2023

Int J Imaging Syst Tech

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Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real-time colorization of the laser surgery video under black-andwhite imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real-time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736.

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Section: Methodsmentioning

confidence: 99%

“…PSNR is a traditional image evaluation method that is often used for quality evaluation after image denoising. 32 The PSNR of the original color image X and the shaded image b X are calculated according to Equations ( 9) and ( 10):…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Real‐time coloring method of laser surgery video based on generative adversarial network

Pan¹,

Lian

et al. 2023

Int J Imaging Syst Tech

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“…For highly correlated image data groups, CAS employs a contentadaptive transform to obtain the sparsest representation, thereby resulting in outstanding denoising performance. By combining a thresholding function and image TV regularization, a modified TV regularization method M-TVRM [25] is proposed for salt and pepper noise (SPN) removal. The thresholding function based on SPN characteristics is utilized for noise pixel detection, and the image TV regularization is employed to restore the noisy pixels.…”

Section: A Model-based Methodsmentioning

confidence: 99%

MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

Lan

Liu

2022

IEEE Access

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Residual histograms can provide valuable information for vision research. However, current image restoration methods have not fully exploited the potential of multiple residual histograms, especially their role as overall regularization constraints. In this paper, we propose a novel framework of multiple residual Wasserstein driven model (MRWM) that can organically combine multiple residual Wasserstein constraints and various natural image priors for image denoising. Specifically, by utilizing the Wasserstein distance derived from the optimal transmission theory, the multiple residual histograms of the observed images are forced to be as close as possible to the reference residual histogram, thereby improving the accuracy of residual estimation. Furthermore, the proposed concrete MRWM unifies the multiple residual Wasserstein distribution approximation and the image total variation prior knowledge to carry out image denoising. Alternating iterative algorithm of histogram matching and Chambolle dual projection has the characteristics of less parameters and easy implementation. Finally, our experiments confirm that compared with some representative image denoising algorithms, the MRWM can obtain better performance in objective evaluation, and can better preserve the details such as the image edges, making the image look more natural.

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“…Some of the methods used for de-noising and recovery of sparse signals are soft thresholding [1], hard thresholding [2], firm thresholding [5], and hyperbolic tangent thresholding [7]. An improved total variation regularization is applied to remove salt and pepper noise from images [36]. Mahdaoui used a compressive sensing approach based on regularization constraints for image de-noising [37].…”

Section: Related Workmentioning

confidence: 99%

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

et al. 2023

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In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.INDEX TERMS Convex optimization, EM algorithm, maximum a posteriori estimator (MAP), MM algorithm, PSNR, RMSE, sparse signal processing.

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Image Noise Removal Method Based on Thresholding and Regularization Techniques

Cited by 13 publications

References 41 publications

Real‐time coloring method of laser surgery video based on generative adversarial network

Real‐time coloring method of laser surgery video based on generative adversarial network

MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

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