Mathematical Modeling and Simulation of low Quality Image Deblurring and Deconvolution
In order to improve the visual effect of images,a mathematical modeling method for deblurring low-quality images based on regularization constraints was put forward.Based on the principle of net function interpolation and diffusion theory,an iterative network function interpolation algorithm was constructed for removing salt-and-pep-per noise.At the same time,the Tetrolrt transform was combined with active random field model to remove Gaussian noise and thus to improve image quality.Based on the sparsity of image blurring kernel and gradient sparsity,a mixed regularization constraint was adopted to construct an estimation mathematical model for both.Then,the model was solved by the alternating direction multiplier method.Moreover,low-quality images were deconvoluted by using the L1 norm and total variation method.Finally,the image deblurring was achieved.Experimental results show that the proposed method achieves higher peak signal-to-noise ratio,structural similarity,average gradient,and visual fidelity of the image after deblurring,as well as clearer image details.
Regularization constraintsLow-quality imageDeblurringNet function interpolation
NETL
NSTL
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