A Novel Wavelet Based Super Resolution Reconstruction of Low Resolution Images Using Adaptive Interpolation

High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. Super resolution reconstruction consists of registration, restoration and interpolation phases, once the Low resolution image are registered with respect to a reference frame then restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. In this paper we are proposing an adaptive interpolation for super resolution reconstruction. Our proposed wavelet based restoration and interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative methods. It reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.