Superresolution reconstruction of images by weighted wavelet bicubic interpolation search algorithm

The variable relationship between the threshold of high-frequency extrapolation and the entropy of its correspondent reconstructed image in the Wavelet Bicubic Interpolation Algorithm is analyzed. The information entropy is used as a cost function and a Maximal Entropy Wavelet Bicubic Interpolation Search Algorithm is proposed. This algorithm can automatically search an extrapolation threshold to reconstruct an image with maximal entropy. Although the detail information of the reconstructed maximal entropy image is larger than its original image, it may introduce a lot of uncertain and incorrect information. In order to remedy this shortcoming of the proposed algorithm, a new cost function based on the old one is established. The new cost function can not only remedy the shortcoming of the entropy function as a cost function, but also a weight introduced in the new cost function can be adjusted to reconstruct different superresolution images to satisfy different practical requirements. Thus a Weighted Wavelet Bicubic Interpolation Search Algorithm is established. The experiment results prove that if the distribution of the processed images is close to the maximum likelihood distribution, a large weight will be selected to reconstruct a relative better superresolution image with better details, and if the distribution of the processed images is far from the maximum likelihood distribution, a little weight will be selected to reconstruct a relative better superresolution image with better visual effect. Therefore, the weight in the new algorithm can be selected from the requirements to satisfy different practical cases.