no code implementations • 10 Mar 2025 • Cansu Korkmaz, Nancy Mehta, Radu Timofte
Recovering high-frequency details and textures from low-resolution images remains a fundamental challenge in super-resolution (SR), especially when real-world degradations are complex and unknown.
1 code implementation • 17 Apr 2024 • Cansu Korkmaz, A. Murat Tekalp
This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field.
1 code implementation • 15 Apr 2024 • Zheng Chen, Zongwei Wu, Eduard Zamfir, Kai Zhang, Yulun Zhang, Radu Timofte, Xiaokang Yang, Hongyuan Yu, Cheng Wan, Yuxin Hong, Zhijuan Huang, Yajun Zou, Yuan Huang, Jiamin Lin, Bingnan Han, Xianyu Guan, Yongsheng Yu, Daoan Zhang, Xuanwu Yin, Kunlong Zuo, Jinhua Hao, Kai Zhao, Kun Yuan, Ming Sun, Chao Zhou, Hongyu An, Xinfeng Zhang, Zhiyuan Song, Ziyue Dong, Qing Zhao, Xiaogang Xu, Pengxu Wei, Zhi-chao Dou, Gui-ling Wang, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Cansu Korkmaz, A. Murat Tekalp, Yubin Wei, Xiaole Yan, Binren Li, Haonan Chen, Siqi Zhang, Sihan Chen, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi, Anjali Sarvaiya, Pooja Choksy, Jagrit Joshi, Shubh Kawa, Kishor Upla, Sushrut Patwardhan, Raghavendra Ramachandra, Sadat Hossain, Geongi Park, S. M. Nadim Uddin, Hao Xu, Yanhui Guo, Aman Urumbekov, Xingzhuo Yan, Wei Hao, Minghan Fu, Isaac Orais, Samuel Smith, Ying Liu, Wangwang Jia, Qisheng Xu, Kele Xu, Weijun Yuan, Zhan Li, Wenqin Kuang, Ruijin Guan, Ruting Deng, Zhao Zhang, Bo wang, Suiyi Zhao, Yan Luo, Yanyan Wei, Asif Hussain Khan, Christian Micheloni, Niki Martinel
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
1 code implementation • CVPR 2024 • Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
Although some recent works focused on the differentiation of details and artifacts, this is a very challenging problem and a satisfactory solution is yet to be found.
no code implementations • 12 Feb 2024 • Cansu Korkmaz, Ege Cirakman, A. Murat Tekalp, Zafer Dogan
This strategy leverages the high-quality image generation capabilities of DMs, while recognizing the importance of obtaining a single trustworthy solution, especially in use cases, such as identification of specific digits or letters, where generating multiple feasible solutions may not lead to a reliable outcome.
no code implementations • 18 Sep 2022 • Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
As a result, the performance of an SR model varies noticeably from image to image over a test set depending on whether characteristics of specific images are similar to those in the training set or not.
no code implementations • 18 Sep 2022 • Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan, Erkut Erdem, Aykut Erdem
We achieve this by benefiting from a diverse set of feasible photo-realistic solutions in the SR space spanned by flow models.
no code implementations • 1 Jun 2021 • Cansu Korkmaz, A. Murat Tekalp, Zafer Dogan
It is well-known that in inverse problems, end-to-end trained networks overfit the degradation model seen in the training set, i. e., they do not generalize to other types of degradations well.
no code implementations • 30 Apr 2021 • Onur Keleş, M. Akin Yilmaz, A. Murat Tekalp, Cansu Korkmaz, Zafer Dogan
Others compute a single PSNR from the arithmetic mean of frame MSEs for each video.