Hyperspectral Image Mixed Noise Reduction Based on Improved K-svd Algorithm

We propose an algorithm for mixed noise reduction in Hyperspectral Imagery (HSI). The hyperspectral data cube is considered as a three order tensor. These tensors give a clear view about both spatial and spectral modes. The HSI provides ample spectral information to identify and distinguish spectrally unique materials, thus they are spectrally over determined. Tensor representation is three ordered thus can simultaneously deal with the two spatial dimensions and one spectral dimension of HSI to achieve a satisfying noise reduction performance. The image analysis application like Classification, unmixing, subpixel mapping and target detection are performed in a very accurate manner due to the development of hyperspectral remote sensing technology as it provides large amount of spatial and spectral information. This entire denoising process is based on the K-SVD denoising algorithm. This method of denoising can efficiently remove a variety of mixed or single noise by applying sparse regularization of small image patches. It also maintains the image texture in a clear manner. The learned dictionary used clearly helps in removing the noise. Our work involved in minimizing model to remove mixed noise such as Impulse noise, Gaussian-Gaussian mixture and Gaussian-Impulse noise from the HSI data. The weighted rank-one approximation problem is solved using a new iterative scheme and the low rank approximation can be obtained by singular value decomposition(SVD).A new weighting data fidelity function which is much easier to optimize is used which has the same minimizer as the original likelihood function. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters.