Author
Hongxun Yao
Bio: Hongxun Yao is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: K-SVD. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.
Topics: K-SVD
Papers
More filters
TL;DR: The gray-consistency & gradient joined diversity-based dictionary representation method is proposed to select the optimal images for the dictionary training and results show that the proposed dictionary selection framework is feasible and effective to improve the quality of sparse reconstruction-based MR super-resolution.
2 citations
TL;DR: This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method to solve the problem of sample selection for dictionary learning of sparse reconstruction and shows that the dictionary- optimized sparse learning improves the performance of sparse representation.
Abstract: Abstract Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.
Cited by
More filters
TL;DR: Wang et al. as discussed by the authors proposed a multi-scale deformable transformer network (MSDT) for multi-contrast knee magnetic resonance imaging super-resolution, which learns the data-dependent sparse attention of the knee MR image, which can adaptively obtain the highfrequency foreground details according to the image content.
3 citations
TL;DR: Experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super- Resolution method effectively improves the super- resolution performance.
Abstract: Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.