Multi-scale error-driven dense residual network for image super-resolution reconstruction.

ABSTRACT: Image super-resolution reconstructs high-resolution images from low-resolution inputs. However, current single-image super-resolution techniques often struggle to capture multi-scale information and extract high-frequency details, which compromises reconstruction quality. Moreover, the prevalent feed-forward network architectures lack robust feedback mechanisms for iterative refinement and enhanced acquisition of high-frequency information. To overcome these limitations, this research develops advanced strategies for multi-scale feature extraction, fusion, and feedback in single-image super-resolution. We propose an innovative error-driven, multi-scale dense residual network (EMDN) that retains a feed-forward structure while integrating error-driven feedback. Specifically, our approach uti

SUBMITTER: Li X 

PROVIDER: S-EPMC12445547 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature