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.

术语超分辨率 (SR) 是指将图像的分辨率从低分辨率 (LR) 提高到高分辨率 (HR) 的应用。但是，出于压缩目的，我们可以在编码期间有意地对图像进行下采样，然后在解码器处使用超分辨率技术对其进行上采样。将此处理集成到压缩方案中将使我们能够通过以下方式进行改进：（1）通过产生受控下采样来减少信息丢失，从而保留相关信息；(2) 通过超分辨率技术增加信息补偿，增强重建图像的高频分量。在本文中，我们提出了一种有损高光谱图像压缩方案。它结合了 3D 小波变换和基于小波学习的超分辨率技术。3D 小波变换在高光谱图像上引入下采样以生成低分辨率 (LR) 图像。该 LR 序列随后由 3D SPIHT 编码器以选定的比特率进行有损压缩，并生成比特流以进行保存或传输。在解码器处，我们通过反转 3D SPIHT 进行重建，这为我们提供了所有子带上 LR 图像的近似系数 (AC) 和相应的细节系数 (DC)。然后，我们开发了一种基于小波学习的 SR 技术，该技术使用卷积神经网络 (CNN) 学习小波系数之间的映射。我们使用这种映射来反转下采样过程并预测缺失的细节系数。最后，通过应用逆 3D 小波变换，LR 图像和预测的细节系数 (DC) 用于生成高分辨率图像。因此，我们使用下采样和超分辨率技术作为控制压缩比（CR）和优化重建图像整体质量的机制。所提出的压缩方案的性能已经在 AVIRIS 高光谱图像上进行了评估，并与现有的主要算法进行了比较。实验结果表明，该算法具有良好的性能，可以生成高质量的图像。所提出的压缩方案的性能已经在 AVIRIS 高光谱图像上进行了评估，并与现有的主要算法进行了比较。实验结果表明，该算法具有良好的性能，可以生成高质量的图像。所提出的压缩方案的性能已经在 AVIRIS 高光谱图像上进行了评估，并与现有的主要算法进行了比较。实验结果表明，该算法具有良好的性能，可以生成高质量的图像。