Multiresolution Wavelet Transform Based Anisotropic Diffusion for Removing Speckle Noise in a Real-Time Vision-Based Database

In this research article, a novel algorithm is introduced to identify the noisy pixels in video frames and correct them to enhance video quality. The technique consists of three stages: fragmentation of the video sequences to respective 2D frames, noisy pixel identification in the 2D frames, and denoising the pixels to obtain original pixels. Due to the complexity in the background and the change in appearance of the body in motion, noise variation occurs. Various researchers discuss that in order to denoise the video sequences, spatio-temporal filtering is required which identifies noise and preserves the edges. In the first stage, the video sequences are analyzed for the removal of redundant frames. This is done by using the video fragmentation process in the MATLAB toolbox. In the next stage, color smoothing is applied to the target frames for processing the flat regions and identifying all the noisy pixels. In the final stage, an improvised multiresolution wavelet transform based anisotropic diffusion filtering is applied which enhances the denoising process in horizontal, vertical, and diagonal sub bands of the video frame signal. The proposed technique can remove the speckle noise and estimate the motion by preserving the minute details of the processed video frames.

Multiresolution continuous wavelet transform for studying coupled solute–solvent vibrations via ab initio molecular dynamics

Solute–solvent coupled vibrations are unveiled using multiresolution wavelet transform of generalized cluster normal modes from ab initio molecular dynamics.