Low-complexity image denoising based on statistical modeling of wavelet coefficients
Citations Over TimeTop 1% of 1999 papers
Abstract
We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.
Related Papers
- → An Effective Signal De-noising Algorithm Combining Optimal Wavelet Packet Basis and Translation-Invariant Algorithm(2008)2 cited
- A LOCALLY SUPPORTED WAVELET WITH INFINITELY HIGH REGULARITY AND FAST ALGORITHM FOR EDGE DETECTION(1999)
- → A New Wavelet Transform Algorithm for Improving the Coefficient Accuracy(2013)
- Continuous Wavelet Transform Circuit Based on Modified Differential Evolution Algorithm(2012)
- The algorithm of multi-level image enhancement based on wavelet transform(2006)