TL;DR
A novel context-aware deep residual network with promotion gates, named as G-CASR network, for SISR, which requires only about 25% memory cost comparing with EDSR to improve the informative ability of extracted context information.
DADE is proposed, a difficulty-aware distillation-enhanced network for efficient super-resolution that substantially reduces the computational cost of super-resolution while maintaining reconstruction quality, with only negligible degradation in PSNR and SSIM, and at the same time greatly lowers the number of stored pa...
This work revisits CNN-based residual learning for SISR and proposes an adaptive residual correction network (AdaRCN), a residual correction mechanism that adaptively compensates for the bias in the residual, which is utilized to ease error accumulation and improve mapping accuracy.
High-resolution image reconstruction is essential in scientific imaging, where preserving structural details directly affects downstream analysis. While Generative Adversarial Networks (GANs) have significantly improved single image super-resolution (SISR), existing approaches often fail to preserve fine structural inf...
Aiming at the problems of large parameters and high computational complexity in deep learning-based image super-resolution networks, this paper proposes a lightweight super-resolution network that fuses multi-attention mechanism and Blueprint Separable Convolution (BSConv). BSConv is introduced to improve performance w...
This paper develops a dual-path generator architecture: the standard path thoroughly extracts deep high-level features, while the auxiliary path preserves prior information and high-frequency details, and introduces a multi-scale feature extraction module to boost the model’s ability to capture features at various scal...
A novel Hierarchical Multi-Scale Cross-Attention Network that effectively captures both local distortion patterns and global semantic information for quality prediction and exhibits superior generalization capability compared to existing approaches is proposed.
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