Method for restoring motion blurred image based on total variational model and neutral network
A motion blurred image and neural network technology, applied in the field of image processing, can solve the problems that the image cannot obtain more details and edge information, and the quality of the restored image is poor, so as to achieve the effects of improving convergence performance, accurate restoration results, and good convergence speed
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[0037] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0038] Step 1, set the Hopfield neural network output error ε, the number of iterations, and the first change amount ΔE of the energy function 1 and the original output, and construct the Toeplitz matrix H, D respectively X and D Y .
[0039] First, set the output error ε of two adjacent Hopfield neural networks and the number of iterations of the Hopfield neural network according to empirical values. The output error ε is generally between 0.05 and 0.1, and the number of iterations is generally 200 to 400 times. Set the Hopfield neural network The first change amount ΔE of the energy function 1 = 0 and use the motion blur image g as the original output x of the Hopfield neural network;
[0040] Secondly, according to the blur scale d and blur angle of the motion blur image Construct the point spread function according to the following formula
[0041] in,
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