Neuro-fuzzy profile clustering in image enhancement

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.

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Neuro-fuzzy profile clustering in image enhancement

Cited by 3 publications

References 11 publications

Low resolution image area classifier based on Convolutional Neural Network

Low resolution image area classifier based on Convolutional Neural Network

The performance of Machine Learning on Low Resolution Image Classifier

Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform

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