Shijie Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Shijie Jiang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ji Zhang
Henan University of Science and Technology
1 shared publication - Qirui Song
School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
1 shared publication - Shuhan Feng
School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
1 shared publication - Zirun Liu
Longmen Laboratory, Luoyang 471000, China
1 shared publication
Latest Publications
- Open Access
Comprehensive Review of Deep Learning Approaches for Single-Image Super-Resolution
Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and explores it from three aspects: theoretical basis, technological evolution, and domain-specific applications. Firstly, the basic concepts, development trajectory, and practical value of SISR are introduced. Secondly, in-depth research is conducted on key technical components, including benchmark dataset construction, a multi-scale upsampling strategy, objective function optimization, and quality assessment indicators. Thirdly, some classic SISR model reconstruction results are listed and compared. Finally, the limitations of SISR research are pointed out, and some prospective research directions are proposed. This article provides a systematic knowledge framework for researchers and offers important reference value for the future development of SISR.
Open AccessComprehensive Review of Deep Learning Approaches for Single-Image Super-Resolution
Single-image super-resolution (SISR) is a core challenge in the field of image processing, aiming to overcome the physical limitations of imaging systems and improve their resolution. This article systematically introduces the SISR method based on deep learning, proposes a method-oriented classification framework, and explores it from three aspects: theoretical basis, technological evolution, and domain-specific applications. Firstly, the basic concepts, development trajectory, and practical value of SISR are introduced. Secondly, in-depth research is conducted on key technical components, including benchmark dataset construction, a multi-scale upsampling strategy, objective function optimization, and quality assessment indicators. Thirdly, some classic SISR model reconstruction results are listed and compared. Finally, the limitations of SISR research are pointed out, and some prospective research directions are proposed. This article provides a systematic knowledge framework for researchers and offers important reference value for the future development of SISR.