An Efficient Remote Sensing Image Denoising Method in Extended Discrete Shearlet Domain
- 558 Downloads
Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design a edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method, it not only can exactly compute the shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. Based on extended DST, an image denoising using fuzzy support vector machine (FSVM) is proposed. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the extended DST. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in extended DST domain, and the FSVM model is obtained by training. Then the extended DST detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by FSVM training model. Finally, the detail subbands of extended DST coefficients are denoised by using the adaptive Bayesian threshold. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.
KeywordsImage denoising Extended discrete Shearlet transform Fuzzy support vector machine Adaptive Bayesian threshold
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272416, 60873222, & 60773031, the Open Project Program of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) under Grant No. 30920130122006, the Open Foundation of Zhejiang Key Laboratory for Signal Processing under Grant No. ZJKL_4_SP-OP2013-01, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. L2013407.
- 1.Vijaya Arjunan, R., Vijaya Kumar, V.: Survey analysis of various image denoising techniques—a perspective view. In: Proceedings of the International Conference on VLSI, Communication and Instrumentation, Kottayam, India, pp. 704–708 (2011) Google Scholar
- 10.Rehman, A., Wang, Z.: SSIM-based non-local means image denoising. In: The 18th IEEE International Conference on Image Processing (ICIP), pp. 217–220 (2011) Google Scholar
- 16.Roth, S., Black, M.J.: Steerable random fields. In: The 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007) Google Scholar
- 21.Li, Y.P., Huttenlocher, D.P.: Sparse long-range random field and its application to image denoising. In: Proceedings of European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science, vol. 5304, pp. 344–357 (2008) Google Scholar
- 23.Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the 1998 IEEE International Conference on Computer Vision, Washington, DC, pp. 839–846 (1998) Google Scholar
- 30.Hu, J., Li, S.: Fusion of panchromatic and multispectral images using multiscale dual bilateral filter. In: The 18th IEEE International Conference on Image Processing (ICIP), pp. 1489–1492 (2011) Google Scholar
- 38.Dong, W., Wu, X., Shi, G.: Context-based bias removal of statistical models of wavelet coefficients for image denoising. In: The 16th IEEE International Conference on Image Processing (ICIP), pp. 3841–3844 (2009) Google Scholar
- 39.Strang, G.: Introduction to Applied Math. Wellesley-Cambridge Press, Wellesley (1986) Google Scholar
- 57.Firouzmanesh, A., Boulanger, P.: Image de-blurring using shearlets. In: The Ninth Conference on Computer and Robot Vision (CRV), pp. 167–173 (2012) Google Scholar