Multivariate Regression CNN for ill-posed Inverse Reconstruction of Satellite Images

摘要

In this paper, we propose Convolutional Neural Network models of Multivariate Regression (MRCNN) applied for the reconstruction of satellite images. Image reconstruction is an ill-posed inverse problem of computer vision. Generally, distortion in satellite images modeled as their convolution with an Airy pattern Point Spread Function (PSF) of circular symmetry and additive white Gaussian noise. The MRCNN training needs a few satellite images, to yield a reconstructed image during testing, with the goals of generalized and optimized restoration. Thus, it must manage a tradeoff between two objectives of optimization and generalization. The image to be restored is not provided to the network during training. The CNN has already proven its effectiveness in image classification and recognition but not thoroughly evaluated on multivariate regression and inverse problems of image processing and computer vision. Therefore, we experimented with different architectures of CNN feasible on a GPU embedded PC for an effective reconstruction of satellite images. The proposed network 5 comprised on three CNN layers and a dense layer with a novel data engineering procedure has shown better reconstruction of satellite images evaluated by peak-signal-to-noise-ratio.