Remote sensing image super‐resolution based on convolutional blind denoising adaptive dense connection

• Published in: IET image processing 2021-09, Vol.15 (11), p.2508-2520
• Main Authors: Yang, Xin, Xie, Tangxin, Guo, Yingqing, Zhou, Dake
• Format: Article
• Language: English
• Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Other topics in statistics
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/ipr2.12236

The current super‐resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory. Therefore, a convolution blind denoising adaptive dense connection SR (CBD‐ADCSR) network for the remote sensing image is proposed in this paper. The whole model is divided into a convolution blind denoising (CBD) network for denoising and an ADCSR network for reconstruction. Firstly, the components of the network are given in detail and are analysed. Secondly, a data set making method is designed combining motion blur, defocusing blur and Gaussian noise, which is used to generate low‐resolution image data sets with complex degradation for the model training. Finally, through the detailed comparative experiment, it is proved that the reconstruction effect of the CBD‐ADCSR model is better than that of the most state‐of‐the‐art algorithms in objective criteria. In addition, compared with the original ADCSR network, CBD‐ADCSR has a stronger ability for noise suppression.