Infrared Image Super-Resolution via Lightweight Information Split Network

no code implementations • 17 May 2024 • Shijie Liu, Kang Yan, Feiwei Qin, Changmiao Wang, Ruiquan Ge, Kai Zhang, Jie Huang, Yong Peng, Jin Cao

A key innovation within this model is the introduction of the Lightweight Information Split Block (LISB) for deep feature extraction.

Image Reconstruction Image Super-Resolution +1

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

1 code implementation • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang

To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.

MRI Reconstruction

LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution

1 code implementation • 22 Jan 2024 • Feiwei Qin, Kang Yan, Changmiao Wang, Ruiquan Ge, Yong Peng, Kai Zhang

Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning.

Image Super-Resolution Infrared image super-resolution

Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks

no code implementations • 8 Jul 2022 • Chunyan Zeng, Kang Yan, Zhifeng Wang, Yan Yu, Shiyan Xia, Nan Zhao

However, when this method uses backpropagation to obtain gradients, it will cause noise in the saliency map, and even locate features that are irrelevant to decisions.