Spectral impulse noise model for spectral image processing

Abstract

The performance of an image processing algorithm can be assessed through its resulting images. However, in order to do so, both ground truth image and noisy target image with known properties are typically required. In the context of hyperspectral image processing, another constraint is introduced, i.e. apart from its mathematical properties, an artificial signal, noise, or variations should be physically correct. Deciding to work in an intermediate level, between real spectral images and mathematical model of noise, we develop an approach for obtaining suitable spectral impulse signals. The model is followed by construction of target images corrupted by impulse signals and these images will later on be used to evaluate the performance of a filtering algorithm.