Super Resolution Reconstruction of Compressed Low Resolution Images Using Wavelet Lifting Schemes

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. The problem of recovering a high resolution image from a sequence of low resolution compressed images is considered. In this paper, we propose lifting schemes for intentionally introducing down sampling of the high resolution image sequence before compression and then utilize super resolution techniques for generating a high resolution image at the decoder. Lifting wavelet transform has its advantages over the ordinary wavelet transform by way of reduction in memory required for its implementation. This is possible because lifting transform uses in-place computation. The lifting coefficients replace the image samples present in the respective memory locations. In our proposed approach the forward lifting is applied to the high resolution images which are compressed using Set Portioning in Hierarchical Trees (SPHIT), the compressed images are transmitted which results in low resolution images at the encoder, at the decoder super resolution techniques are applied; lifting scheme based fusion is performed, then decoded using DSPIHT and Inverse lifting is applied. In order to remove noise from the reconstructed image soft thresholding is performed and the blur is removed using blind deconvolution, and finally interpolated using our novel 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.