Local Polynomial Approximation-Intersection of Confidence Intervals (LPA-ICI) is a new approach, which can find the
boundary of the isotropic region efficiently, especially for noisy images. This paper presents a novel image denoising
method, adaptive four windows wavelet image denoising based on LPA-ICI, which is composed of three parts: searching
for four adaptive windows with LPA-ICI, updating the noisy wavelet coefficients by hard threshold and obtaining a final
"clean" pixel value by fusing the updated pixels with different weights which are determined by the sparsity of regions.
Experiments show that our algorithm has advanced performance, reconstructed edges are clean, and especially without
unpleasant ringing artifacts.
As one important multiresoltion geometry analysis tool, second generation bandelets can make full use of intrinsic
geometry regularity of images, and then produces a sparse representation. This paper proposes a new denoising method,
which is based on second generation bandelets and improved adaptive proportion-shrinking algorithm. Experiments on
natural images with additive Gaussian white noise show that our method not only has the high peak signal to noise
ratio(PSNR) value, but also has finer impression in vision, especially, has better performance on preservation of edges
information and textures information than the classical proportion-shriking algorithm.