Spatio-spectral Image Reconstruction Using Non-local Filtering

TL;DR

This paper presents a novel non-local filtering approach for spatio-spectral image reconstruction that effectively models inter-band relationships, especially in high-frequency regions, outperforming local methods.

Contribution

It introduces a non-local filtering technique for linear regression-based spatio-spectral reconstruction, improving results in high-frequency regions.

Findings

Increases PSNR by approximately 2 dB on average.

Produces visually more appealing images in high-frequency regions.

Outperforms local neighborhood-based methods.

Abstract

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.