Image denoising is an important image processing task, both as a process itself and as a component in other processes. Images are often corrupted by noise during the acquisition process. Denoising aims at eliminating this measurement noise while trying to preserve important signal features such as texture and edges. Over the past few decades, a large variety of algorithms has been developed for that purpose. Image denoising is an important process in image processing. In previous approach there are two denoising approach has been implemented such as NLM and Block- Matching and 3D (BM3D).In Non Local Means (NLM) each pixel has been estimated separately and fusing other "similar" neighborhood pixel. Whereas Block-Matching and 3D filtering (BM3D) is performed on group of similar patches together. In this paper, our proposed approach is a Global denoising algorithm (GLIDE) which is a non-local patch-based Processing where each pixel is estimated from all pixels in the image. Approximated spectral decomposition is computed using Nystrom Method. Eigen decomposition of the approximated image is estimated by Sinkhorn method for getting orthogonal eigenvectors of the obtained Eigen values with the help of Orthogonalization process. Finally Denoised image will be obtained using this proposed strategy. Hence, our approach can effectively enhance the performance of existing filters.