Super-Resolution and De-convolution for Single/Multi Gray Scale Images Using SIFT Algorithm
Keywords:
Image restoration, SIFT Algorithm, super-resolution, blind estimation, blind de-convolution, Huber Markov Random Field, Alternating MinimizationAbstract
This paper represent a Blind algorithm that restore the blurred images for single image and multi-image blur de-convolution and multi-image super-resolution on low-resolution images deteriorated by additive white Gaussian noise ,the aliasing and linear space-invariant. Image De-blurring is a field of Image Processing in which recovering an original and sharp image from a corrupted image. Proposed method is based on alternating minimization algorithm with respect to unidentified blurs and high-resolution image and the Huber-markov random field(HMRF) model for its ability to preserve discontinuities of a image and used for the regularization that exploits the piecewise smooth nature of the HR image. SIFT algorithm is used for feature extraction in a image and produce matching features based on Euclidean distance of their feature vectors that help in calculation of PSF. For blur estimation, edge-emphasizing smoothing operation is used to improve the quality of blur by enhancing the strong soft edges. In filter domain the blur estimation process can be done rather than the pixel domain for better performance that means which uses the gradient of HR and LR images for better performance.
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