A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

Dutta, Sayantan; Basarab, Adrian; Georgeot, Bertrand; Kouamé, Denis

doi:10.1016/j.sigpro.2022.108690

Cited by 21 publications

(11 citation statements)

References 43 publications

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“…Recently, some scholars have proposed deep-unfolding denoising [38][39][40] and quantumbased denoising [41,42], which have achieved competitive results compared to state-of-theart image denoising tasks. How to draw on the ideas of these methods to denoise the point cloud is a very valuable research work in the future.…”

Section: Related Workmentioning

confidence: 99%

A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds

Gao,

Wang,

Zhao

2024

Entropy

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In the acquisition process of 3D cultural relics, it is common to encounter noise. To facilitate the generation of high-quality 3D models, we propose an approach based on graph signal processing that combines color and geometric features to denoise the point cloud. We divide the 3D point cloud into patches based on self-similarity theory and create an appropriate underlying graph with a Markov property. The features of the vertices in the graph are represented using 3D coordinates, normal vectors, and color. We formulate the point cloud denoising problem as a maximum a posteriori (MAP) estimation problem and use a graph Laplacian regularization (GLR) prior to identifying the most probable noise-free point cloud. In the denoising process, we moderately simplify the 3D point to reduce the running time of the denoising algorithm. The experimental results demonstrate that our proposed approach outperforms five competing methods in both subjective and objective assessments. It requires fewer iterations and exhibits strong robustness, effectively removing noise from the surface of cultural relic point clouds while preserving fine-scale 3D features such as texture and ornamentation. This results in more realistic 3D representations of cultural relics.

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Section: Related Workmentioning

confidence: 99%

A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds

Gao,

Wang,

Zhao

2024

Entropy

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“…Filtering or Denoising Algorithm Selection: Once the noise characteristics are understood, the next step is to choose an appropriate denoising algorithm [27,28] or filter. The choice depends on factors such as the type and intensity of noise, the desired level of noise reduction, and the importance of preserving image details.…”

Section: Procedures Of Image Denoisingmentioning

confidence: 99%

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

2023

Preprint

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This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

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“…They then used the principal texture representation to estimate the noise principal components and compute the principal component via image texture variance. Dutta et al [ 32 , 33 ] used quantum mechanical concepts to represent and process signals and images, proposing novel algorithms and frameworks that bring new ideas and approaches to the field of image noise research.…”

Section: Introductionmentioning

confidence: 99%

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

Xie,

Yi,

Yang

2023

Sensors

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Blind noise level estimation is a key issue in image processing applications that helps improve the visualization and perceptual quality of images. In this paper, we propose an improved block-based noise level estimation algorithm. The proposed algorithm first extracts homogenous patches from a single noisy image using local features, obtaining the covariance matrix eigenvalues of the patches, and constructs dynamic thresholds for outlier discrimination. By analyzing the correlations between scene complexity, noise strength, and other parameters, a nonlinear discriminant coefficient regression model is fitted to accurately predict the number of redundant dimensions and calculate the actual noise level according to the statistical properties of the elements in the redundancy dimension. The experimental results show that the accuracy and robustness of the proposed algorithm are better than those of the existing noise estimation algorithms in various scenes under different noise levels. It performs well overall in terms of performance and execution speed.

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A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

Cited by 21 publications

References 43 publications

A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds

A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

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