Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising

Shen, Yan; Liu, Qing; Lou, Shuqin; Hou, Ya-Li

doi:10.1109/lsp.2017.2688707

Cited by 53 publications

(19 citation statements)

References 32 publications

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“…We use the proposed denoising method for simulated noisy sonar image, and compare it with several methods. The denoising comparison algorithms used in this paper include Lee filter [36], Kuan filter [37], Frost filter [38], SRAD filter [39], Wavelet-based denoising method [14], Curvelet-based denoising method [15], DCT-based denoising method [12], and K-SVD denoising method [34]. Figure 4 shows the original image and simulated noisy image.…”

Section: Denoising Results Of Simulated Noisy Imagementioning

confidence: 99%

“…Although the spatial domain filtering method has achieved a certain denoising effect, there are certain drawbacks: the processed image is usually too smooth, and it can easily cause blurred edge and detail loss. However, the another transform domain filtering method contains common Discrete Cosine Transform (DCT) [12], principal component analysis (PCA) [13], and Wavelet denoising algorithms [14]. Based on wavelet transform, a direction parameter was added and the Curvelet transform threshold filtering was used for noise removal [15].…”

Section: Introductionmentioning

confidence: 99%

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Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Huang

Yuan

2020

JMSE

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As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved.

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Section: Denoising Results Of Simulated Noisy Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Huang

Yuan

2020

JMSE

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“…A variety of image denoising methods can be roughly categorised as follows: traditional methods, such as filtering‐based methods [1, 2], total variation‐based methods [3, 4], non‐local self‐similarity (NSS)‐based methods [5, 6]; learning‐based methods, like dictionary learning‐based methods [7, 8], neural network‐based methods [9, 10]. Among them, NSS‐based image restoration technology has drawn much attention because of its simplicity and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Multi‐scale patches based image denoising using weighted nuclear norm minimisation

Xiang

et al. 2020

IET image process

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“…Its iterative character allows for improved computational efficiency, but speckled or mosaic effects appear in the denoised CT images [6][7]. Noise reduction algorithms in the transformation domain are based on different multiscale transformations, with the possible use of noise assessment [8][9][10][11].CT image is decomposed in the low and high frequency sub-bands of the scale space. Usually, the high-frequency wavelet coefficients are subjected to threshold processing because the noise components are located mainly in these subbands.…”

Section: Introductionmentioning

confidence: 99%

CT image denoising based on locally adaptive thresholding

Petrov

2019

SRIT

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The noise in reconstructed X-ray Computed Tomography (CT) slices is complex, non-stationary and indefinitely distributed. Subsequent image processing is needed in order to achieve a good-quality medical diagnosis. It requires a sufficiently great ratio between the detailed contrasts and the noise component amplitude. This paper presents an adaptive method for noise reduction in CT images, based on the local statistical evaluation of the noise component in the domain of Repagular Wavelet Transformation (RWT). Considering the spatial dependence of the noise strength, the threshold constant for processing the high frequency coefficients in the proposed shrinkage method is a function of the local standard deviation of the noise for each pixel of the image. Experimental studies have been conducted using different images in order to evaluate the effectiveness of the proposed algorithm.

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Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising

Cited by 53 publications

References 32 publications

Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

Multi‐scale patches based image denoising using weighted nuclear norm minimisation

CT image denoising based on locally adaptive thresholding

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