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

A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising