Super-resolution reconstruction using wavelet transform

Because of a deterioration in resolution, the distant parts of images taken by acamera are of an inferior quality. Super-resolution is one of the effective methods to improvethe quality of low resolution images. In super-resolution, a high resolution-image is generatedfrom multiple low resolution images by image processing such as inverse discrete wavelettransformation. Inverse discrete wavelet transform can expand the image by doubling theresolution. Super-resolution by inverse discrete wavelet transform needs three different highfrequency components in the image to be improved. For accurate expansion by inversediscrete wavelet transform, these high frequency components must be accurate. However, it isdifficult to estimate these components because of the sensitivity of the transform toward themargin of error. This is why there are few methods for improving images by using wavelettransform. Therefore, we have tried to reduce this domain to estimate. Estimation of highfrequency components becomes easy, if we can improve low resolution images after thedomain has been reduced. In this paper, we have tried to limit the domain of these highfrequency components.