Diffusion-Based Inpainting for Coding Remote Sensing Data

Inpainting techniques based on partial differential equations (PDEs), such as diffusion processes, are gaining growing importance as a novel family of image compression methods. Nevertheless, the application of inpainting in the field of hyperspectral imagery has been mainly focused on filling in missing information or dead pixels due to sensor failures. In this letter, we propose a novel PDE-based inpainting algorithm to compress hyperspectral images. The method inpaints separately the known data in the spatial and spectral dimensions. Then, it applies a prediction model to the final inpainting solution to obtain a representation much closer to the original image. Experimental results over a set of hyperspectral images indicate that the proposed algorithm can perform better than a recent proposed extension to prediction-based standard CCSDS-123.0 at low bit rate, better than JPEG 2000 Part 2 with the DWT 9/7 as a spectral transform at all bit rates, and competitive to JPEG 2000 with principal component analysis, the optimal spectral decorrelation transform for Gaussian sources.