Image Denoising Using Adaptive PCA and SVD

James, Rithu; Jolly, Anita Mariam; Anjali, C; Michael, Dimple

doi:10.1109/icacc.2015.82

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…In the PCA transform domain, the scene detail information is concentrated on several important components, and the scene detail irrelevant information is evenly distributed throughout all components [20,21].We chose sample vectors similar enough to the central vector, meaning the information of the central vector and its local structure accounts for the largest amount of information retained by the PCA. Therefore, we can extract the scene detail content better.…”

Section: Extracting Interference Noise From the Reference Prnumentioning

confidence: 99%

A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

Liu

et al. 2023

Applied Sciences

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Photo response non-uniformity (PRNU) is a widely accepted inherent fingerprint that has been used in source camera identification (SCI). However, the reference PRNU noise is limited by the performance of PRNU noise extraction techniques and is easily contaminated by interfering noise from image content. The existing methods mainly suppressed the interference noise of the reference PRNU noise in the spectral domain, but there was still interference noise related to the image content in the low-frequency region. We considered that this interference noise of the reference PRNU noise could be removed by further operations in the spatial domain. In this paper, we proposed a scheme to distil the reference PRNU by removing the interference noise with the help of principal component analysis (PCA) technology. Specifically, the reference PRNU noise was modelled as white Gaussian noise, whereas the interfering noise caused correlation between pixels and their neighbourhoods in the reference PRNU noise. In the local pixel area, we modelled a pixel and its neighbours as a vector and used block matching to select PCA training samples with similar contents. Next, PCA transformation estimated the interference noise in the local pixel area, and we performed coefficient shrinkage in the PCA domain to better estimate interference noise. The experimental results on the “Dresden” and “VISION” datasets showed that the proposed scheme achieved better receiver operating characteristic curves and the Kappa statistic than state-of-the-art works.

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Section: Extracting Interference Noise From the Reference Prnumentioning

confidence: 99%

A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

Liu

et al. 2023

Applied Sciences

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“…As explained in Section 2.2, the singular values of each mode (SV 1 , SV 2 , SV 3 ) can be calculated from Σ. Similarly to the 2D case, by picking the relevant components having a singular value higher than a threshold R n , a denoised version of Y , Ŷ may be achieved [16].…”

Section: Proposed Td-sisrmentioning

confidence: 99%

Single Image Super-Resolution Of Noisy 3d Dental Ct Images Using Tucker Decomposition

Hatvaniy

Michetti

Basarab

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Tensor decomposition has proven to be a strong tool in various 3D image processing tasks such as denoising and superresolution. In this context, we recently proposed a canonical polyadic decomposition (CPD) based algorithm for single image super-resolution (SISR). The algorithm has shown to be an order of magnitude faster than popular optimization-based techniques. In this work, we investigated the added value brought by Tucker decomposition. While CPD allows a joint implementation of the denoising and deconvolution steps of the SISR model, with Tucker decomposition the denoising is realized first, followed by deconvolution. This way the illposedness of the deconvolution caused by noise is partially mitigated. The results achieved using the two different tensor decomposition techniques were compared, and the robustness against noise was investigated. For validation, we used dental images. The superiority of the proposed method is shown in terms of peak signal-to-ratio, structural similarity index, the canal segmentation accuracy, and runtime.

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“…This further encourages us to think whether PCA can be used to dramatically reduce the dimensionality of image patch vectors before evaluating their similarity. Based on the above consideration, some works 36,37 have been done.…”

Section: Pca-subspace Euclidean Distancementioning

confidence: 99%

An improved nonlocal sparse regularization-based image deblurring via novel similarity criteria

Wang

Shi

Fan

et al. 2018

International Journal of Advanced Robotic Systems

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Image deblurring is a challenging problem in image processing, which aims to reconstruct an original high-quality image from its blurred measurement caused by various factors, for example, imperfect focusing caused by the imaging system or different depths of scene appearing commonly in our daily photos. Recently, sparse representation whose basic idea is to code an image patch as a linear combination of a few atoms chosen out from an overcomplete dictionary has shown uplifting results in image deblurring. Based on this and another heart-stirring property called nonlocal self-similarity, some researchers have developed nonlocal sparse regularization models to unify the local sparsity and the nonlocal self-similarity into a variational framework for image deblurring. In such models, the similarity evaluation for searching similar image patches is indispensable and influential in deblurring performance. Though the traditional Euclidean distance is generally a choice as a similarity metric, its application might give rise to inferior performance since it fails to capture the intrinsic structure of image patches. Consequently, in this article, based on structural similarity index and principal component analysis, we propose the nonlocal sparse regularization-based image deblurring with novel similarity criteria called structural similarity distance and principal component analysis-subspace Euclidean distance to improve the accuracy of deblurring. The structural similarity index is commonly used for assessing perceptual image quality, and principal component analysis is pervasively used in pattern recognition and dimensionality reduction. In our comprehensive experiments, the nonlocal sparse regularization-based image deblurring with our novel similarity criteria has achieved higher peak signal-to-noise and favorable consistency with subjective vision perception compared with state-of-the-art deblurring algorithms.

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Image Denoising Using Adaptive PCA and SVD

Cited by 11 publications

References 7 publications

A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

Single Image Super-Resolution Of Noisy 3d Dental Ct Images Using Tucker Decomposition

An improved nonlocal sparse regularization-based image deblurring via novel similarity criteria

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