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 utilizes dual multi-scale features: one derived from convolutional kernels of varying sizes and another extracted from diverse inputs, both processed concurrently. Comparative evaluations across different scaling factors demonstrate that our method outperforms existing approaches in both subjective and objective assessments. In particular, compared to the baseline feed-forward network, our model achieves improvements of up to 0.385% in peak signal-to-noise ratio and 0.191% in structural similarity index measure. The experimental results validate the effectiveness and practical significance of our proposed method in enhancing image resolution and restoration quality.