UAV Image denoising using adaptive dual-tree discrete wavelet packets based on estimate the distributing of the noise

Abstract

Unmanned Aerial Vehicles (UAV) digital images are often badly degraded by noise during dynamic acquisition and transmission process. Denoising is very important and difficult for UAV-vision Guided, because natural scene image is complicated and having lots of the edges and texture details. The image denoising algorithm based on adaptive dual-tree discrete wavelet packets(ADDWP) which combine the dual-tree discrete wavelet transform(DDWT) and the wavelet packets is proposed in this paper. In ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decompoisition structure, we using the signal-to-noise ratio to estimate the distributing of the denoising in order to search the more denoising subbands to decomposition it again. So we can get adaptive decompoisition structure of wavelet packets. The new algorithm has significantly lower computational complexity than a previously developed optimal basis selection algorithm. For denoising the ADDWP coefficients, a statistical model is used to exploit the relation of the coefficients in order to distinguish the noise and the signal. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. The visual quality of images denoised by the proposed scheme is also superior.