An Efficient Image Deblurring Method with a Deep Convolutional Neural Network for Satellite Imagery

Satellite imagery acquired from optical remote sensing systems is often suffered due to several types of blur, such as atmospheric turbulence blur, motion blur, and defocus. Any kind of blur degrades the image quality, as it reduces the sharpness of edges and texture, and hence, spatial resolution is also reduced. This degradation poses a challenge for further automated analysis using such blurred images. Image deconvolution methods are conventionally applied to blurry images to estimate the blur and restore the original image. In this inverse and ill-posed problem, the restoration quality relies on the correct estimation of the point spread function that caused blur in the image. We aim to restore the blurred satellite imagery, corrupted by Gaussian blur, using the deep learning framework and hence prove the efficacy of the proposed method over the traditional methods. This paper presents an investigative analysis of the satellite image deblurring problem and simultaneously tackles the problem of low-resolution satellite imagery. A deep convolutional neural network (CNN) architecture is proposed to remove the Gaussian blur artifacts from images and increase their resolution. Experimental evaluation is performed on a hyperspectral image dataset consisting of 8 different terrain types. Results obtained after deblurring the images are compared with state-of-the-art methods based on both subjective and objective image quality measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The image quality of deblurred images obtained by the proposed deep CNN method demonstrates improved performance over some of the existing CNN methods.