TL;DR
The proposed Progressive Multi-branch Distillation Attention Network (PMDAN), a lightweight SR framework based on a progressive dilated feature extraction strategy, achieves competitive reconstruction performance among recent lightweight SR methods.
Significant progress has been made in remote sensing image super-resolution based on deep neural networks. However, existing methods typically suffer from parameter redundancy and high computational costs, making them difficult to deploy on resource-constrained edge devices. Moreover, the image reconstruction process o...
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...
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...
Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational cost of Transformer-based SR approaches. However, existing Mamba-based methods lack fine-grained transitions across different modeling scale...
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