Medical Image Super Resolution Based On Dual Tree Complex Wavelet Transform

Abstract Most natural images can be approximated using their low-rank components. This fact has been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitrary combination of other rows or columns with known values. While increasing the size of the image the original image quality will be affected. In order to avoid the loss of quality while enhancing the image, Low rank optimization based on TV and Non Local Means (NLM) Optimized Sparse Method is used. The resulting is then further enhanced with the help of DTCWT. The noises and the pixel differences occurring in the up sampling and down sampling of the images were identified and they were removed based on the proposed method. The resulting images were then further enhanced with the help of DTCWT. In DTCWT DWT, SWT, SVD decomposition was used. Using DTCWT the image size is further increased and also the image is enhanced. This process is more applicable for medical images since the loss in the original pixel information's were well preserved. The performance of the proposed method is proved using the performance parameters Citation: N.Arivazhaki, KLN College of Information Technology ; S.Anbhumozhi ,. "Medical Image Super Resolution Based On Dual Tree Complex Wavelet Transform." Global Research and Development Journal For Engineering : 403 - 407.