Guided Filtering of Hyperspectral Images

Abstract

Guided image filter (GIF) is an efficient edge-preserving image filter which has numerous applications in image processing. The filtered output of GIF is locally a linear transform of a guide image, which can be the input image itself or a different image. The key feature of GIF is that it does not suffer from gradient reversal artifacts. In this article, we extend the concept of guided image filtering to the context of hyperspectral imaging. We consider a new (linear) model to perform the joint guided filtering of all the spectral components at a time at each pixel. Our proposed technique involves computation of matrix-inversion just once; regardless of the number of spectral bands of the image to be filtered. Thus, bypassing the redundant computations in the original proposal of GIF, we introduce a fast variant of guided filtering algorithm. The proposed algorithm produces exactly same filtered output as GIF. Experimental results demonstrate the effectiveness of the proposed variant of GIF. Finally, we extend our filter to a variety of hyperspectral imaging applications - edge preserving smoothing, detail enhancement and denoising.