Local adaptive transform based image denoising with varying window size

Local adaptive image denoising in transform domain is a powerful tool for adapting to unknown smoothness of the images. We propose to perform local adaptive denoising with adaptively varying local transform support size rather than using a transform with fixed size. We use a special rule (intersection of confidence intervals-ICI) to select the optimum window sizes locally. The algorithm provides significant improvements in the de-noising performance