Super Resolution Image Reconstruction using Wavelet Lifting Schemes and Gabor filters

The main objective of the super resolution images is to enhance the quality of the multiple lower resolution images. Super Resolution image is constructed by using raw images. An Image with improved resolution is always desirable for various applications like satellite, medical etc. to enhance the qualitative features are the images. In this paper, Super Resolution Image Reconstruction (SRIR) is proposed for improving the resolution of lower resolution images. Proposed approach is described as follows. Initially, Some low resolution images of same scene which are usually translated, rotated and blurred are used to form a super resolution image. Then, the image registration operation translated, orients, scaled and rotated images in the similar way to that of source image. Next, Lifting Wavelet Transform (LWT) with Daubechies4 coefficients are applied to color components of each image due to its less memory allocation compared to other wavelet techniques. Further, Set Portioning in Hierarchical Trees (SPIHT) technique is applied for image compression as it possess lossless compression, fast encoding/decoding, and adaptive nature. The three low resolution images are fused by spatial image fusion method. The noise is removed by dual tree Discrete Wavelet Transform (DWT) and blurring is reduced by blind deconvolution. Finally, the samples are interpolated to original samples to obtain a super resolution image. The structural similarity for each intermediate image is compared to the source image to observe high structural similarity by objective analysis. Gabor transform is also implemented for image enhancement and edge detection.