Hyperspectral images (HSIs) are often
corrupted by noise during an acquisition process, e.g.,
Gaussian noise, salt and pepper noise, deadlines, strip,
and many others. This project proposes an image
restoration algorithm based on Higher Order Singular
Value Decomposition algorithm (HOSVD). The HOSVD
acts as fast preconditioner. Instead of dealing as pixels
the image is processed as tensor. This project reduces
the noises that occur in remote sensing satellite images.
The satellite images are degraded by the noise such as
Gaussian noise, Impulse noise, Strip noise etc. The
tensor of the degraded image is added with tensor of
Gaussian, Impulse, Strip noise. The Singular values are
extracted from the degraded image which controls
intensity of Gaussian, Impulse and Strip noise tensor.
This process gets repeated iteratively to get the restored
image. The higher order singular value decomposition
(HOSVD) of the degraded tensor is obtained very fast
and so could be used as a preconditioner. Iterative
median filtering for restoration of images corrupted by
mixed noise is proposed. The boundary condition for
the iteration is based on minimum distance between any
two successive iterations is less than a threshold value.
Experimental results show that proposed system has
higher convergence speed. The complexity of an image
restoration process reduces highly further we measures
Peak Signal Noise Ratio (PSNR) and Mean Square
Error (MSE). The PSNR values appear to be high while
the MSE values appear to be low.
Keywords : Image Restoration, Hyperspectral Image (HSI), Mixed Noise, HOSVD, Iterative Median Filter.