A new deblurring morphological filter for hyperspectral images

Abstract

Hyperspectral imaging becomes an important technique that increases the valuable information enclosed within the image. Spectral cube produced by this type of imaging introduces a new material signature known as "spectral signature". This signature is unique for each material as it depends on the molecular composition of the material surface. To produce the spectral cube, a spectrometer should be used in the imagery device to split the electromagnetic energy at different wavelengths before its projection on the imaging array. This spectrometer may be a dispersive element, such as prism and grating, or an electronically tuneable filter. Some of dispersive spectrometers, such as Fourier transform interferometer (FTIR) and image multi-spectral imaging (IMSS), are based on sliding the lenses, or mirrors, along the optical axis which may result in a slightly out-of-focus blurring. Blind deconvolution techniques have been successfully used to decrease this blurring but at the expense of edge sharpening which may be a problem in some applications such as target detection and recognition.In this paper, we introduce a new method to deblurr the hyperspectral images keeping edges as sharp as possible. This is done by firstly detecting the edges locations and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators are used to detect the locations of these edges followed by an adaptive morphological filter to sharpen these detected edges. Experimental results demonstrate that the performance of the proposed deblurring filter is superior to that of the blind deconvolution methods.