Enhancing hyperspectral image compression using learning-based super-resolution technique

The term super-resolution (SR) refers to the applications of enhancing the resolution of an image from low-resolution (LR) to high-resolution (HR). However, for compression purposes, we can intentionally down-sample the image during encoding and then up-sample it with a super-resolution technique at the decoder. Integrating this processing in a compression scheme will allow us to perform improvements through: (1) reducing the loss of information by producing a controlled down-sampling, which preserves the relevant information; (2) increasing the information compensation via a super-resolution technique that enhances the high-frequency components of the reconstructed image. In this paper, we propose a lossy hyperspectral image compression scheme. It combines a 3D wavelet transform with a wavelet learned-based super-resolution technique. The 3D wavelet transform introduces a down-sampling on the hyperspectral image to generate low-resolution (LR) images. This LR sequence is then lossy compressed by the 3D SPIHT encoder at the selected bitrate and produces the bitstream to save or transmit. At the decoder, we do the reconstruction by inverting the 3D SPIHT, which provides us with the approximate coefficients (AC) and corresponding detail coefficients (DC) of the LR image across all sub-bands. Then, we develop a wavelet learning-based SR technique that learns the mapping between the wavelet coefficients using a convolutional neural network (CNN). We use this mapping to invert the down-sampling process and predict the missing detail coefficients. Finally, The LR image and the predicted details coefficients (DC) are used to generate the high-resolution image by applying the inverse 3D wavelet transform. Therefore, we have used the down-sampling and the super-resolution technique as a mechanism to control the compression ratio (CR) and optimize the overall quality of the reconstructed image. The performance of the proposed compression scheme has been evaluated on AVIRIS hyperspectral images and compared with the main existing algorithms. Experimental results show that the proposed algorithm provides a promising performance and can generate high-quality images.