Learning Detail-Structure Alternative Optimization for Blind Super-Resolution

Feng LI, Yixuan WU, Huihui BAI*, Weisi LIN, Runmin CONG, Yao ZHAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)


Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown degradations in real-world applications. Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation. However, their results still remain visible artifacts and detail distortion due to the estimation errors. To alleviate these problems, in this paper, we propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. Specifically, in our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. The DSMM consists of two components: a detail restoration unit (DRU) and a structure modulation unit (SMU). The former aims at regressing the intermediate HR detail reconstruction from LR structural contexts, and the latter performs structural contexts modulation conditioned on the learned detail maps at both HR and LR spaces. Besides, we use the output of DSMM as the hidden state and design our DSSR architecture from a recurrent convolutional neural network (RCNN) view. In this way, the network can alternatively optimize the image details and structural contexts, achieving co-optimization across time. Moreover, equipped with the recurrent connection, our DSSR allows low- and high-level feature representations complementary by observing previous HR details and contexts at every unrolling time. Extensive experiments on synthetic datasets and real-world images demonstrate that our method achieves the state-of-the-art against existing methods.

Original languageEnglish
Pages (from-to)2825-2838
Number of pages14
JournalIEEE Transactions on Multimedia
Early online date25 Feb 2022
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1999-2012 IEEE.


  • Blind image super-resolution
  • convolutional neural networks
  • detail restoration
  • detail-structure alternative optimization
  • structure modulation


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