Experimental Study on Image Reconstruction from Spatial Correlation-based Optical Flow Motion Vector over Non Gaussian Noise Contamination using Reversed Confidential with Bilateral Filter

Abstract

In motion estimation, noise is a verity to degrade the performance in optical flow for determining motion vector. This paper examines the performance of noise tolerance model in spatial correlation-based optical flow for image reconstruction from motion vector where the source sequences are contaminated by non Gaussian noise. There are Poisson Noise, Salt & Pepper Noise, and Speckle Noise. In the experiment, several standard sequences in different styles are used and the applied combination model of reversed confidential with bilateral filter on spatial correlation-based optical flow is mainly focused to determined the best condition to apply this model with. The result in image reconstruction from motion vector is used in performance comparison with traditional noise tolerance models by using Peak Signal to Noise Ratio (PSNR) as a primary index for studying.