Gibbs-ringing artifact suppression with knowledge transfer from natural images to MR images

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c -means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images.

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Blind Image Deblurring with Modified Richardson-Lucy Deconvolution for Ringing Artifact Suppression

Advances in Image and Video Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-642-25346-1_22 ◽

2011 ◽

pp. 240-251 ◽

Cited By ~ 4

Author(s):

Hao-Liang Yang ◽

Yen-Hao Chiao ◽

Po-Hao Huang ◽

Shang-Hong Lai

Keyword(s):

Image Deblurring ◽

Blind Image Deblurring ◽

Ringing Artifact ◽

Artifact Suppression ◽

Blind Image

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Nonconvex Compressed Sampling of Natural Images and Applications to Compressed MR Imaging

ISRN Computational Mathematics ◽

10.5402/2012/982792 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Wenze Shao ◽

Haisong Deng ◽

Zhuihui Wei

Keyword(s):

Natural Images ◽

Reconstruction Algorithms ◽

Mr Images ◽

Signal To Noise ◽

Visual Effects ◽

High Quality ◽

Structure Preserving ◽

Compressed Sampling ◽

Imaging Reconstruction ◽

Nyquist Rate

There have been proposed several compressed imaging reconstruction algorithms for natural and MR images. In essence, however, most of them aim at the good reconstruction of edges in the images. In this paper, a nonconvex compressed sampling approach is proposed for structure-preserving image reconstruction, through imposing sparseness regularization on strong edges and also oscillating textures in images. The proposed approach can yield high-quality reconstruction as images are sampled at sampling ratios far below the Nyquist rate, due to the exploitation of a kind of approximate l0 seminorms. Numerous experiments are performed on the natural images and MR images. Compared with several existing algorithms, the proposed approach is more efficient and robust, not only yielding higher signal to noise ratios but also reconstructing images of better visual effects.

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Attention-based Convolutional Neural Network for MRI Gibbs-ringing Artifact Suppression

10.51130/graphicon-2020-2-3-34 ◽

2020 ◽

pp. paper34-1-paper34-12

Author(s):

Maksim Penkin ◽

Andrey Krylov ◽

Alexander Khvostikov

Keyword(s):

Neural Network ◽

Fourier Transform ◽

Convolutional Neural Network ◽

High Frequency ◽

Gibbs Phenomenon ◽

Linear Mapping ◽

Full Spectrum ◽

Non Linear ◽

Ringing Artifact ◽

Artifact Suppression

Gibbs-ringing artifact is a common artifact in MRI image processing. As MRI raw data is taken in a frequency domain, 2D in- verse discrete Fourier transform is applied to visualize data. Inability to take inverse Fourier transform of full spectrum (full k-space) leads to the insufficient sampling of the high frequency data and results in a well-known Gibbs phenomenon. It is worth to notice that truncation of high frequency information generates a significant blur, thus some techniques from other image restoration problems (for example, image deblur task) can be successfully used. We propose attention-based convolutional neural network for Gibbs-ringing reduction which is the extension of recently proposed GAS-CNN (Gibbs-ringing Artifact Suppression Convolutional Neural Network). Proposed method includes simplified non-linear mapping, amended by LRNN (Layer Recurrent Neural Network) refinement block with feature attention module, controlling the correlation between input and output tensors of the refinement unit. The research shows that the proposed post-processing refinement construction considerably simplifies the non-linear mapping.

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