Observation Model based Perceptually Motivated Bilateral Filter for Image Reconstruction

Recently, a lot of bilateral and non-local means based reconstruction algorithms are proposed in literature. The success of these filters lies in finding similar patches in the neighbourhood. However, in complex regions and in presence of noise, finding similar patches based on mean square error (MSE) is not reliable. This results into blurred edges and visible patches in the reconstructed image. To address this issue, we propose a new Observation model based and Perceptually Motivated Bilateral Filter (OPBIF) for Image Reconstruction. In which image quality assessment (IQA) matrices are used to find the similarity among the patches. From experimental results, it is validated that the proposed algorithm has the capability of reconstructing sharp edges, as compared to existing non-linear filtering algorithms.