Multiresolution Bilateral Filtering for Image Denoising

• Published in: IEEE transactions on image processing Vol. 17; no. 12; pp. 2324 - 2333
• Main Authors: Zhang, Ming, Gunturk, Bahadir K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Colored noise; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filter bank; Filtering; Image denoising; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image resolution; Imaging, Three-Dimensional - methods; Information, signal and communications theory; Least squares approximation; Low-frequency noise; Miscellaneous; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal resolution; Signal, noise; Spatial resolution; Telecommunications and information theory; Temperature sensors; Averaging method; Subband decomposition; Filtering; Optimal filter; Image processing; Threshold detection; Noise reduction; Non linear filter; Low frequency; Multiresolution analysis; Noisy image; Wavelet transformation; Signal processing; Filter bank; Image denoising
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.2006658

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.