A bivariate shrinkage function for complex dual tree DWT based image denoising
WAMUS'06 Proceedings of the 6th WSEAS international conference on Wavelet analysis & multirate systems
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Feature-based wavelet shrinkage algorithm for image denoising
IEEE Transactions on Image Processing
Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising
IEEE Transactions on Circuits and Systems for Video Technology
A Real-Time Technique for Spatio–Temporal Video Noise Estimation
IEEE Transactions on Circuits and Systems for Video Technology
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With the widespread use of videos in many fields of our lives, it becomes very important to develop new techniques for video denoising. Spatial video denoising using wavelet transform has been the focus of the current research, as it requires less computation and more suitable for real-time applications. Two specific techniques for spatial video denoising using wavelet transform are considered in this work: 2D Discrete Wavelet Transform 2D DWT and 2D Dual Tree Complex Wavelet Transform 2D DTCWT. We performed an analytical analysis to investigate the performance of each of these techniques. From this analysis, we found out that each of these techniques has its advantages and disadvantages. The first technique gives less quality at high levels of noise but consumes less time, whereas the second gives high quality video while consuming a large amount of time. In this work, we introduce an intelligent denoising system that makes a tradeoff between the quality of the denoised video and the time required for denoising. The system first estimates the noise level in the video frame then chooses the proper denoising technique to apply on the frame. The simulation results show that the proposed system is more suitable for real-time applications where time is critical, while still giving high quality videos at low to moderate levels of noise.