Image super resolution reconstruction method on basis of Contourlet transformation
A super-resolution reconstruction and super-resolution technology, which is applied in the field of image super-resolution reconstruction based on Contourlet transform, can solve the problems that the image edge details are difficult to reproduce, and the geometric information of the image is not used, so as to achieve clear edges and complex algorithms low degree of effect
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[0020] In the imaging stage of the image, it is inevitably affected by factors such as noise and blur, so the obtained image is a degraded image, and its mathematical model is shown in formula (1):
[0021] x L =f*x H +n (1)
[0022] x H Indicates the original high-resolution image, f is the system blur, and n is the noise. x L is the low-resolution image after systematic blurring and noise pollution. * indicates the convolution process. x L can be represented by a Contourlet transformation Ψ as a sparser coefficient s:
[0023] x L =Ψs(2)
[0024] combined with Figure 1-2 , an image super-resolution reconstruction method based on Contourlet transform includes the following steps.
[0025] Step 1, for the known low-resolution image x L Perform Contourlet transformation
[0026] Assuming a low resolution image x L The size of is 256*256, and the Contourlet transformation is performed on it to obtain the coefficient s of the transformed Contourlet domain. The Con...
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