4.7 Article

Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling

Journal

REMOTE SENSING
Volume 14, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/rs14122895

Keywords

remote sensing images (RSIs); super resolution (SR); real-world degradation; deep learning (DL); balanced attention

Funding

  1. National Natural Science Foundation of China [61527802, 61371132, 61471043]
  2. China Postdoctoral Science Foundation [BX20200051]
  3. Natural Science Foundation of Chongqing [cstc2021jcyj-msxmX1130]

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Limited resolution is a major obstacle to the application of remote sensing images (RSIs). This paper proposes a method to build a more realistic training dataset by modeling the degradation with blur kernels and imaging noises, and designs a novel residual balanced attention network (RBAN) to improve the super-resolution of images. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance in quantitative evaluation and visual quality.
Limited resolution is one of the most important factors hindering the application of remote sensing images (RSIs). Single-image super resolution (SISR) is a technique to improve the spatial resolution of digital images and has attracted the attention of many researchers. In recent years, with the advancement of deep learning (DL) frameworks, many DL-based SISR models have been proposed and achieved state-of-the-art performance; however, most SISR models for RSIs use the bicubic downsampler to construct low-resolution (LR) and high-resolution (HR) training pairs. Considering that the quality of the actual RSIs depends on a variety of factors, such as illumination, atmosphere, imaging sensor responses, and signal processing, training on ideal datasets results in a dramatic drop in model performance on real RSIs. To address this issue, we propose to build a more realistic training dataset by modeling the degradation with blur kernels and imaging noises. We also design a novel residual balanced attention network (RBAN) as a generator to estimate super-resolution results from the LR inputs. To encourage RBAN to generate more realistic textures, we apply a UNet-shape discriminator for adversarial training. Both referenced evaluations on synthetic data and non-referenced evaluations on actual images were carried out. Experimental results validate the effectiveness of the proposed framework, and our model exhibits state-of-the-art performance in quantitative evaluation and visual quality. We believe that the proposed framework can facilitate super-resolution techniques from research to practical applications in RSIs processing.

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