An Innovative Approach for Denoising of the Digital Video Stream

Everyday tones of video signals are generated, transmitted, and analyzed. The video contents are created for educational purposes, entertainment purposes, surveillance purposes, medical imaging purposes, weather forecasting, satellite imaging, and many other significant places. During the different phases of video content preparation, transmission, and analysis some unwanted signals get interfered with the true contents. Particularly, the medical imaging signals, since they are weak signals, are more prone to unwanted interferences. Such unwanted interference of the noise signals makes it difficult to analyze the critical information in the video contents and hence, the need for denoising process arises. A decent video denoising framework assures visual improvement in the video signals or it serves as the significant pre-processing step in the video processing steps like compression and analysis. Through this paper, we are about to disclose an efficient video denoising framework that takes the noisy video signal in the form of frames per second and performs the video denoising using shot detection, compensation, intensity calculations, and motion estimation process.

Iterative based image and video denoising by fractional block matching and transform domain filtering

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

Iterative based image and video denoising by fractional block matching and transform domain filtering

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.