Abstract
Image super-resolution (SR) reconstruction plays a key role in coping with the increasing demand on remote sensing imaging applications with high spatial resolution requirements. Though many SR methods have been proposed over the last few years, further research is needed to improve SR processes with regard to the complex spatial distribution of the remote sensing images and the diverse spatial scales of ground objects. In this paper, a novel multi-perception attention network (MPSR) is developed with performance exceeding those of many existing state-of-the-art models. By incorporating the proposed enhanced residual block (ERB) and residual channel attention group (RCAG), MPSR can super-resolve low-resolution remote sensing images via multi-perception learning and multi-level information adaptive weighted fusion. Moreover, a pre-train and transfer learning strategy is introduced, which improved the SR performance and stabilized the training procedure. Experimental comparisons are conducted using 13 state-of-the-art methods over a remote sensing dataset and benchmark natural image sets. The proposed model proved its excellence in both objective criterion and subjective perspective.
Highlights
Super-resolution (SR), which aims at restoring the missing high-frequency information from lower-resolution images in order to increase the apparent spatial resolution [1], is a crucial field of research in the remote sensing community
Evaluation metrics: Experimental results are quantitatively evaluated with peak signal- to-noise ratio (PSNR) and the structural similarity index (SSIM) [42] on the Y channel in transformed YCbCr space
After adding residual channel attention group (RCAG), a similar trend is observed—the performance improved from 39.540 dB to 39.604 dB. These findings firmly demonstrate the effectiveness of widely extracting and reasonably leveraging multi-level prior information by introducing the proposed enhanced residual block (ERB) and RCAG
Summary
Super-resolution (SR), which aims at restoring the missing high-frequency information from lower-resolution images in order to increase the apparent spatial resolution [1], is a crucial field of research in the remote sensing community. Once a remote sensing satellite is launched, the super-resolving reconstruction is needed to exceed those limitations and improve the image resolution from a post-processing perspective. SR, as a key image processing technique, has gained increasing attention for decades. Many traditional algorithms have been proposed to handle this issue [4,5,6]. With the booming of deep learning-based methods and the satisfying results they gained, traditional algorithms are outperformed by them. Deep learning-based super-resolving networks could be categorized into two groups according to their structures: linear networks and skip connection-based networks
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