Development of Image Super-resolution Algorithms

Abstract

This thesis addresses the problem of image super-resolution from a low resolution image. Spatial resoltuion of images are restricted by the size of CMOS sensors. Spatial resolution can be increased by increasing no of COMS sensors resuling in decrease in size of CMOS sensors which cause shot noise. In this thesis attempts have been made to enhance the spatial resolution of different images. Two schemes are proposed for this purpose. The basic idea behind both the techniques is to utilize the high frequency subband images derived using lifting wavelet transform. In the first scheme the high frequency subband images are interpolated using surface fitting. In another scheme lifting wavelet transform and stationary wavelet transform are used along with surface fitting interpolation to increase the spatial resolution in the frequency domain. Each technique is studied separately, and experiments are conducted to evaluate their performances. The visual, blind image quality index, visual image fidelity index as well as the peak signal to noise ratio (PSNR in dB) of high resolution images are compared with competent recent schemes. Experimental results demonstrate that the proposed approaches are very effective in increasing resolution and compare favorably to state-of-the-art super-resolution algorithms.