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Image denoising based on the fractional-order total variation and the minimax-concave
The total variation model has attracted considerable attention for its good balance of noise reduction and edge maintenance, but it produces blocky...
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A bilevel optimization problem with deep learning based on fractional total variation for image denoising
In this work, we introduce a bilevel problem based on fractional-order total variation for image denoising. A deep learning architecture is provided...
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An improved method for image denoising based on fractional-order integration
Given that the existing image denoising methods damage the texture details of an image, a new method based on fractional integration is proposed....
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A comprehensive review of image denoising in deep learning
Deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used....
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Edge detection using the Prewitt operator with fractional order telegraph partial differential equations (PreFOTPDE)
Detecting edges in image processing is an important process in image analysis or enhancement. Many methods detected edge information based on the...
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Image restoration via combining a fractional order variational filter and a TGV penalty
In this paper, a novel variational model for the restoration of images contaminated with multiplicative noise (also known as speckle) is proposed. It...
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A multi-attention Uformer for low-dose CT image denoising
Kee** the number of projection views constant and reducing the radiation dose at each view is an effective way to achieve low-dose CT. This will...
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An Approach for Denoising of Contaminated Signal Using Fractional Order Differentiator
Calculus of integer order is more than a part of our daily life. As the order deviates to the fractional realm, things become much more interesting....
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Deep learning-based RGB-thermal image denoising: review and applications
Recently, vision-based detection (VD) technology has been well-developed, and its general-purpose object detection algorithms have been applied in...
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Tensor-guided learning for image denoising using anisotropic PDEs
In this article, we introduce an advanced approach for enhanced image denoising using an improved space-variant anisotropic Partial Differential...
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Hyperspectral image denoising based on multi-resolution dense memory network
Hyperspectral images (HSIs) denoising is an important pre-processing step since noise will seriously degrade the HSIs quality. In this paper, we...
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Non-monotone Boosted DC and Caputo Fractional Tailored Finite Point Algorithm for Rician Denoising and Deblurring
Since MRI is often corrupted by Rician noise, in medical image processing, Rician denoising and deblurring is an important research. In this work,...
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Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely...
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Improved Gegenbauer spectral tau algorithms for distributed-order time-fractional telegraph models in multi-dimensions
The distributed-order fractional telegraph models are commonly used to describe the phenomenas of diffusion and wave-like anomalous, which can model...
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SW/SE-CNN: semi-wavelet and specific image edge extractor CNN for Gaussian image denoising
Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using...
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Multi-level Fisher vector aggregated completed local fractional order derivative feature vector for face recognition
In this paper, we propose an image feature extraction method, multi-level Fisher vector aggregated completed local fractional order derivative...
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Triple discriminators - equipped GAN for Denoising of Chinese calligraphic tablet images
Denoising of Chinese calligraphic tablet images is of great importance in regard to the study of both content and character shapes in these images....
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A novel image denoising approach based on a curvature-based regularization
The classical total variation (TV) model has made great successes in image denoising due to the edge-preserving property of the TV regularization....
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Controllable Deep Learning Denoising Model for Ultrasound Images Using Synthetic Noisy Image
Medical ultrasound imaging has gained widespread prevalence in human muscle and internal organ diagnosis. Nevertheless, various factors such as the...
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Investigation of key parameters to define stop condition in image denoising algorithms based on the diffusion equation
The first use of diffusion equation for denoising images was first proposed back 1990. Although such an approach showed a remarkable performance,...
