k-means based hybrid wavelet and curvelet transform approach for denoising of remotely sensed images

This article presents a new technique for denoising of remotely sensed images based on multi-resolution analysis (MRA). Multi-resolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image processing and analysis. The multi-resolution image analysis methods have the ability to analyse the image in an adaptive manner, capturing local as well as global information. Further, noise, as one of the biggest obstacles for image analysis and for further processing, is effectively handled by multi-resolution methods. The article aims at the analysis of noise filtering of image using wavelets and curvelets methods on multispectral images acquired by the QuickBird and medium-resolution Landsat Thematic Mapper satellite systems. To improve the performance of noise filtering, an iterative thresholding scheme and a hybrid approach based on wavelet and curvelet transforms are proposed for restoring the image from its noisy version. Two comparative measures are used for evaluation of the performance of the methods for denoising. One of them is the peak signal-to-noise ratio and the second is the ability of the noise filtering scheme to preserve the sharpness of the edges. By both of these comparative measures, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas with iterative threshold has proved to be better than the others. Results are illustrated using QuickBird and Landsat images for proposed methods and compared with wavelets and curvelet-based denoising.