In order to preserve the texture and edge information and to improve the space resolution of single frame, a superresolution algorithm based on Contourlet (NSCT) is proposed. The original low resolution image is transformed by NSCT, and the directional sub-band coefficients of the transform domain are obtained. According to the scale factor, the high frequency sub-band coefficients are amplified by the interpolation method based on the edge direction to the desired resolution. For high frequency sub-band coefficients with noise and weak targets, Bayesian shrinkage is used to calculate the threshold value. The coefficients below the threshold are determined by the correlation among the sub-bands of the same scale to determine whether it is noise and de-noising. The anisotropic diffusion filter is used to effectively enhance the weak target in the low contrast region of the target and background. Finally, the high-frequency sub-band is amplified by the bilinear interpolation method to the desired resolution, and then combined with the high-frequency subband coefficients after de-noising and small target enhancement, the NSCT inverse transform is used to obtain the desired resolution image. In order to verify the effectiveness of the proposed algorithm, the proposed algorithm and several common image reconstruction methods are used to test the synthetic image, motion blurred image and hyperspectral image, the experimental results show that compared with the traditional single resolution algorithm, the proposed algorithm can obtain smooth edges and good texture features, and the reconstructed image structure is well preserved and the noise is suppressed to some extent.
[1]
Ying Wu,et al.
Super-Resolution Without Dense Flow
,
2012,
IEEE Transactions on Image Processing.
[2]
Wei Zhang,et al.
A no-reference contourlet-decomposition-based image quality assessment method for super-resolution reconstruction
,
2014,
Photonics Asia.
[3]
Minh N. Do,et al.
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
,
2006,
IEEE Transactions on Image Processing.
[4]
Huang Wei,et al.
Super-resolution image reconstruction based on sparse threshold
,
2016
.
[5]
Wei Yanxin.
Image Super-resolution Reconstruction Algorithms Based on Self-similarities and Dictionary Learning
,
2013
.
[6]
Zhang Nan.
Using weighted parabolic interpolation to zoom images based on an error-amended sharp edge algorithm
,
2011
.
[7]
Minh N. Do,et al.
Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation
,
2022
.
[8]
Yue Linwe.
A Bilateral Structure Based Local Adaptive Regularization for Super-resolution
,
2015
.