Authors: Yong Tian; Jing Wang; Yunfeng Zhang
Addresses: Langfang People's Air Defense Office, Langfang 065000, Beijing, China ' School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Guangyang, Langfang 065000, China ' School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Guangyang, Langfang 065000, China
Abstract: This paper proposes an efficient hybrid framework for image denoising, in which the advantages of different denoising methods are effectively incorporated by using the region prior knowledge. In detail, the input noisy image is first divided into a large number of overlapping patches followed by extraction of Speed Up Robust Feature (SURF), and then the noisy patches are classified into two categories based on Twin Support Vector Machine (TWSVM). The texture patches are enhanced via Gradient Histogram Preservation (GHP) while flat patches can be re-established using Block Matching Three-Dimensional Filtering (BM3D). Finally, the re-established images can be acquired by fusing the processing results of the two kinds of patches. To evaluate the effectiveness of the presented method, we conduct experiments on standard image datasets and compare the performance with other outstanding denoising approaches. Experimental results show that the presented method achieves better results, especially in containing textures and edges compared with existing image denoising methods.
Keywords: image denosing; hybrid; SURF; speed up robust feature; TWSVM; twin support vector machine; clustering; GHP; gradient histogram preservation; BM3D; block matching three-dimensional filtering; region division; PSNR; peak signal-to-noise ratio; MSE; mean squared error.
International Journal of Computer Applications in Technology, 2020 Vol.64 No.3, pp.308 - 315
Received: 09 Jun 2020
Accepted: 17 Jun 2020
Published online: 16 Dec 2020 *