  Due to the factors like processing power limitations and channel capabilities
images are often down sampled and transmitted at low bit rates resulting in a
low resolution compressed image. High resolution images can be reconstructed
from several blurred, noisy and down sampled low resolution images using a
computational process know as super resolution reconstruction. Super-resolution
is the process of combining multiple aliased low-quality images to produce a
high resolution, high-quality image. The problem of recovering a high
resolution image progressively from a sequence of low resolution compressed
images is considered. In this paper we propose a novel DCT based progressive
image display algorithm by stressing on the encoding and decoding process. At
the encoder we consider a set of low resolution images which are corrupted by
additive white Gaussian noise and motion blur. The low resolution images are
compressed using 8 by 8 blocks DCT and noise is filtered using our proposed
novel zonal filter. Multiframe fusion is performed in order to obtain a single
noise free image. At the decoder the image is reconstructed progressively by
transmitting the coarser image first followed by the detail image. And finally
a super resolution image is reconstructed by applying our proposed novel
adaptive interpolation technique. We have performed both objective and
subjective analysis of the reconstructed image, and the resultant image has
better super resolution factor, and a higher ISNR and PSNR. A comparative study
done with Iterative Back Projection (IBP) and Projection on to Convex Sets
(POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that
our method has out performed the previous contributions.
