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Article
Mathematics, Applied
Wenhui Lian et al.
Summary: In this paper, we propose a novel non-convex extended scheme for reconstructing blurred images under impulse noise. By combining non-convex p-norm pound and fractional-order total variation regularization, our method effectively addresses the staircase-like aspects and preserves clear contours. Experimental comparisons with popular methods demonstrate the superiority of our proposed model in terms of image restoration.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Chunwei Tian et al.
Summary: This paper proposes a multi-stage image denoising CNN with wavelet transform, using dynamic convolution, wavelet transform and enhancement, and residual block to improve denoising performance. Experimental results show that the proposed method outperforms popular denoising methods.
PATTERN RECOGNITION
(2023)
Article
Operations Research & Management Science
Fan Jiang et al.
Summary: This paper discusses a class of common saddle point problems and proposes a simple primal-dual algorithm with larger stepsizes compared to existing algorithms. Furthermore, the algorithm includes the well-known primal-dual hybrid gradient method and has the potential to derive partially linearized primal-dual algorithms. Finally, the algorithm is shown to be capable of handling multi-block separable saddle point problems, with a parallel algorithm proposed for a specific application.
JOURNAL OF GLOBAL OPTIMIZATION
(2023)
Article
Multidisciplinary Sciences
Minmin Li et al.
Summary: In this paper, an improved image-denoising algorithm based on the TV model is proposed, which reduces the number of estimated parameters and achieves global adaption of regularization parameters through inverse gradient estimation. The algorithm shows better performance in preserving image texture structure and suppressing image noise.
Article
Engineering, Multidisciplinary
Benxin Zhang et al.
Summary: This paper proposes a nonconvex log total variation model for image restoration, and presents a specific alternating direction method of multipliers to solve the model. Experimental results demonstrate that the proposed method is effective in image denoising, deblurring, computed tomography, magnetic resonance imaging, and image super-resolution.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Multidisciplinary
Qiaohong Liu et al.
Summary: This paper proposes a non-convex fractional-order variational model for single image blind restoration, which constrains the image using quaternion FTV and L-p quasinorm, and the blur kernel using L-1 norm, and solves the non-convex problem using alternating direction minimization algorithm. Experimental results demonstrate the effectiveness and superiority of the proposed method.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Biomedical
A. Smitha et al.
Summary: A novel retinex driven non-local TGV model is proposed to restore and enhance speckled images, utilizing a balancing parameter and detailed statistical analysis. The model is designed to handle speckle noise and improve contrast features without distorting natural image characteristics. A fast numerical approximation scheme is employed to enhance computational efficiency, and the model is verified to outperform state-of-the-art models in despeckling and enhancing Optical Coherence Tomography (OCT) data.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Tran Dang Khoa Phan et al.
Summary: This paper proposes a second-order model based on edge coherence weighting for image denoising, which can overcome the blurring of object edges caused by high-order variational models.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Environmental Sciences
Andreia Valentina Miclea et al.
Summary: Obtaining accurate classification results for hyperspectral images requires high-quality data and carefully selected samples and descriptors for training and testing. This study proposes a machine learning framework for hyperspectral image classification, which includes denoising and enhancement techniques, as well as a parallel approach for feature extraction. The proposed approach combines spectral and spatial features using a Support Vector Machine classifier. Experimental results on three public datasets demonstrate the effectiveness of the proposed approach, especially in terms of avoiding biased classification results caused by overlapping between training and testing datasets.
Article
Engineering, Electrical & Electronic
Sayantan Dutta et al.
Summary: This paper proposes a novel image denoising algorithm that exploits an image-dependent basis inspired by quantum mechanics. By formalizing similarity measures in local image neighborhoods through terms similar to interaction, the algorithm can effectively preserve the local structures of real images. The adaptive nature of the basis expands its application to image-independent or image-dependent noise scenarios.
Review
Computer Science, Information Systems
K. A. Saneera Hemantha Kulathilake et al.
Summary: Noise is a significant degradation of medical images that hinders the diagnostic process in clinical medicine. This study reviews and classifies the various self-adaptive approaches and techniques implemented in recent medical image denoising applications, identifying the limitations of existing denoising algorithms and suggesting future research directions.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Geochemistry & Geophysics
Yang Chen et al.
Summary: In this article, we propose a new approach for denoising hyperspectral images (HSI) by simultaneously modeling the HSI prior and noise characteristics. The method incorporates a non-i.i.d. mixture of Gaussian (MoG) assumptions, a nonlocal low-rank model, and an adaptive edge-preserving total variation (TV) regularization term. Experimental results on simulated and real data demonstrate the superior performance of the proposed method compared to state-of-the-art approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Sayantan Dutta et al.
Summary: The paper introduces a novel image denoising algorithm that exploits image-dependent basis and a concept similar to interaction in quantum mechanics to efficiently preserve the local structures of real images. The algorithm is versatile and can adapt to image-independent or image-dependent noise scenarios.
Article
Computer Science, Interdisciplinary Applications
Sutanu Bera et al.
Summary: The study aimed to address common bottlenecks in low dose CT denoising by proposing novel methods such as utilizing neighborhood similarity of CT images, introducing noise aware mean square error loss, and a new discriminator function. These methods performed remarkably better than existing state of the art methods on a publicly available dataset.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Optics
Xiaobo Zhang
Summary: This paper introduces a novel center weight calculation method WFCW and reveals the relationship between non-local total variation and NLM. Experimental results demonstrate that the proposed method achieves better performance and is more efficient compared to other NLM methods.
Article
Mathematics, Applied
Fan Jiang et al.
Summary: This paper investigates a first-order inexact primal-dual algorithm for solving convex-concave saddle point problems. The algorithm demonstrates convergence rate and speed, with many practical problems satisfying the convergence conditions.
NUMERICAL ALGORITHMS
(2021)
Article
Engineering, Electrical & Electronic
Juncheng Guo et al.
Summary: Image denoising models based on total variation (TV) regularization are widely used in image processing, effectively preserving edges. The proposed nonconvex anisotropic total variation (NCATV) model describes local features robustly with a weighted matrix dependent on the restored image, requiring decoupling with a successive replacement scheme. By using the alternating direction method of multipliers (ADMM), the proposed model transforms into easily solvable subproblems, showing improved performance visually and quantitatively compared to state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Ying Wen et al.
Summary: A hybrid BM3D and PDE method for non-parametric single image denoising was proposed to address the artificial and bias effects of the BM3D method. The NLPM-NLE and NPSID methods were introduced to automatically and effectively remove noise while preserving details, showing promising results in real image denoising experiments.
Article
Engineering, Electrical & Electronic
Kaicong Sun et al.
Summary: This paper proposes a regularization technique named bilateral spectrum weighted total variation (BSWTV) to address the oversmoothness and residual noise issues in noisy-image super-resolution and image denoising. By introducing locally adaptive shrink coefficient and eigenvalues of the covariance matrix, the weighting map is effectively refined to suppress the residual noise and improve image quality.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Sayantan Dutta et al.
Summary: The study investigates a new approach for constructing signal or image-dependent bases inspired by quantum mechanics tools, considering them as potentials in the discretized Schroedinger equation. Experimental results demonstrate the potential of this decomposition method for denoising under Gaussian, Poisson, and speckle noise compared to other state of the art algorithms.
IEEE OPEN JOURNAL OF SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Zhi-Feng Pang et al.
Article
Optics
Phan Tran Dang Khoa
Article
Computer Science, Information Systems
Hongyao Deng et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
V. B. Surya Prasath et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tingting Zhao et al.
Article
Engineering, Electrical & Electronic
Dong Yang et al.
IEEE SIGNAL PROCESSING LETTERS
(2018)
Article
Computer Science, Interdisciplinary Applications
Meng Li et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2018)
Article
Engineering, Electrical & Electronic
Po-Wen Hsieh et al.
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Computer Science, Artificial Intelligence
Minh Phuong Nguyen et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2017)
Article
Mathematics, Applied
Xian-Ming Gu et al.
JOURNAL OF SCIENTIFIC COMPUTING
(2017)
Article
Operations Research & Management Science
Mingqiang Li et al.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
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Article
Mathematics, Applied
Wen YouWei et al.
SCIENCE CHINA-MATHEMATICS
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Article
Engineering, Electrical & Electronic
Jinming Duan et al.
DIGITAL SIGNAL PROCESSING
(2016)
Article
Computer Science, Artificial Intelligence
Virginia Estellers et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2015)
Article
Computer Science, Information Systems
Dan Tian et al.
INFORMATION SCIENCES
(2015)
Article
Computer Science, Artificial Intelligence
You-Wei Wen et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2012)
Article
Computer Science, Artificial Intelligence
Antonin Chambolle et al.
JOURNAL OF MATHEMATICAL IMAGING AND VISION
(2011)
Article
Operations Research & Management Science
A. Nedic et al.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
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Article
Mathematics, Applied
T Chan et al.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
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