Video denoising using surfacelet transform

The conventional video denoising algorithms utilizes either a strenuous motion estimation step or by three dimensional wavelet transformation. However, these schemes of video denoising results in videos with jittery edges and curves. The limitations of motion estimation based schemes are that they suffer due to aperture problems in optical flow and lighting variations. Yue M. Lu and Minh N. Do introduced a potent tool for representing multidimensional signals called surfacelet transform. Video sequences considered as a different class of 3D signal can be processed using surfacelet transform which preserve the edge information and visual quality. Our work focusses on the development of an efficient video processing algorithm utilizing surfacelet transform. Different thresholds for video denoising using surfacelet transformation were also studied. The algorithm exhibited superior denoising results using Bayes shrink threshold validated on the basis of peak signal to noise ratio and structural similarity index.