In the real world, abrupt changes caused by external environment and system interaction can exhibit a pattern resembling an “impulse” response. A novel model, known as the hybrid-index model, has recently been introduced for Boolean control ...

• Forward completeness can be disregarded in solving control problems of IBCNs.
• The analysis of optimal control problems are notably simplified by quotient mapping.
• Optimal control problems of IBCNs is solved by using a recursive ...

This paper proposes an iterative score-based generative model for solving the automatic colorization problem. Although unsupervised learning methods have shown the capability to generate plausible color, inadequate exploration of detailed ...

With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in the absence of high-quality, fully sampled datasets for training, the ...

Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction. Recently, various supervised deep learning-based MRI reconstruction methods have been developed. Despite the achieved promising ...

Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the performance is limited owing to the high-dimension of data manifold and model ...

Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited recently. Despite the achieved inspiring results, the optimization ...

Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to the ...

The progress of convolution neural network (CNN) based super-resolution has shown its potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) provides the possibility to accelerate the traditional acquisition process ...

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This paper presents a supervised data-driven algorithm for impulse noise removal via iterative scheme-inspired network (IIN). IIN is defined over a data flow graph, which is derived from the iterative procedures in Alternating Direction Method of ...

Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. ...

Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In this paper, we propose a ...

The morbidity of brain stroke increased rapidly in the past few years. To help specialists in lesion measurements and treatment planning, automatic segmentation methods are critically required for clinical practices. Recently, approaches based on ...

Recent researches have shown that deep convolutional neural networks can significantly boost the performance of single-image super-resolution (SISR). In particular, residual network and densely convolutional network can improve performance ...

Illustration of the VST-Net where the channel number of the last Conv layer at SubNet1 and SubNet2 is 1. “C”, “B” and “R” stands for the Conv, BN and ReLU layers, respectively.

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• A novel Poisson denoising network based on convolutional neural network is proposed.

Poisson noise occurs in various applications including medical imaging and night vision. There exist many traditional Poisson denoising algorithms, particularly, one class of impressive algorithm is based on variance-stabilizing ...

Image inpainting is a classical yet challenging inverse ill-posed problem. In this paper, we introduce the multi-filters guided low-rank tensor coding as a prior information to tackle it. The key innovation is to formulate multiple ...

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• The method introduces the multi-filters guided low-rank tensor coding as a prior information for image inpainting.

• Color image is converted to be grayscale by CNN via gradient domain modeling.
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This paper shows how to convert a color image to grayscale using convolutional neural network (CNN), that preserves visual contrast via gradient domain modeling. We propose to explore the auxiliary variable principle to make the input ...

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This paper presents a novel semi-reference inspired color-to-gray conversion model for faithfully preserving the contrast details of the color image, essentially differs from most of the no-reference and reference approaches. In the proposed model, on ...

Convolutional neural networks (CNNs) have presented their potential in filling large missing areas with plausible contents. To address the blurriness issue commonly existing in the CNN-based inpainting, a typical approach is to conduct texture refinement ...

Reducing low-dose radiation while maintaining high-quality image reconstruction in X-ray computed tomography (CT) is challenging, due to the reconstruction images degradation as the number of projection view decreases. As opposed to most of the existing ...

Most low-rank tensor approximations are NP-hard problems. In this paper, we introduce a novel concept: field-of-experts (FoE) filters guided tensor completion, which aims to integrate the strengths of the emerging tensor completion method and the ...