Panchromatic image based dictionary learning for hyperspectral imagery denoising

Sparse coding based noise reduction algorithms have been extensively applied on hyperspectral imagery (HSI) denoising. Dictionary learning schemes are strongly suggested for sparse reconstruction in many researches, aiming at a smaller error between the underlying clean image and the reconstruction result. In previous researches, the training samples (patches) are selected from either unrelated clean images or the noised image itself. The dictionaries learned form unrelated clean images can not perfectly represent the underlying clean target image, while the dictionaries learned form the noised image itself may be affected by the noise existing in training samples. In this paper, we propose a novel dictionary learning scheme that depends on a panchromatic image from the same or similar scene with HSI. Considering the fact that the noise level of a panchromatic image is always much lower than HSI, we take the patches from panchromatic image as training samples. Taking the multi-scale image representation into consideration, we construct the dictionary from different scales via Gaussian pyramid. The proposed dictionary shows its good denoising performance in our experiments.