A Deep Learning-Based Joint Image Super-Resolution and Deblurring Framework

Deep 4 learning (DL)-based single image super-resolution (SISR) for low-resolution (LR) images typically aims to recover a high-resolution (HR) image from its LR version due to downsampling and blurring imperfections of the imaging sensor. The existing DL SR networks reasonably solve the downsampling problem, however, they do not address the complex deblurring problem, simultaneously. To address the later, we propose a joint dual-branch convolutional neural network (CNN) for recovering sharp HR images from LR images degraded with Gaussian blur. The proposed method has two task-independent networks: 1) super-resolution (SR) and 2) deblurring. In particular, we adopt a residual spatial and channel squeeze-and-excitation (RSCSE) module incorporating concurrent spatial and channel squeeze-and-excitation (SCSE) attention mechanism and local feature fusion (LFF) concepts in the SR network. Furthermore, the deblurring network is designed based on a SCSE-based encoder-decoder module to retrieve sharp features from blurred LR images. The feature maps obtained from these networks are adaptively fused by learning a gated module with attention mechanism to generate a clear HR. Experimental results demonstrate that the proposed method outperforms other state-of-the-art DL techniques in visual results and quantitative metrics; peak signalto-noise ratio (PSNR) improves by 1.4 dB-4.9 dB and 0.4 dB-2.6 dB for zooming factors 2 and 4, respectively, on publicly available RGB remote sensing (RS) datasets. Similarly, for multispectral (MS) datasets, they are 1.4 dB-3.5 dB and 0.2 dB-1.4 dB for zooming factors 2 and 4, respectively. It also provides promising results for land cover classification in RS applications.