IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 9, SEPTEMBER 2010 2307 Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal Chih-Hsing Lin, Jia-Shiuan Tsai, and Ching-Te Chiu Abstract—In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allo- cate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter in- side the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and- pepper with Gaussian noise. In addition, the computational com- plexity of SBF is significantly less than that of other mixed noise filters. Index Terms—Gaussian noise, image restoration, impulse noise, mixed noise, nonlinear filters, switch bilateral filter, switching scheme. I. INTRODUCTION N OISE is introduced into images during acquisition, signal amplification and transmission [6], [27]–[31]. An impor- tant problem of image processing is to effectively remove noise from an image while keeping its features. Noise removal is a dif- ficult task because images may be corrupted by different types of noise, such as additive, impulse or signal dependent noise [27]–[29]. The solution depends upon the type of noise added to the image [28]–[30]. Linear filtering possesses mathematical simplicity and offers satisfactory performance on images with additive Gaussian noise [29]–[31]. However, linear techniques blur edges and fail for non-Gaussian and/or impulse noise. This Manuscript received September 02, 2009; revised March 02, 2010; accepted March 20, 2010. First published April 08, 2010; current version published Au- gust 18, 2010. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Kiyoharu Aizawa. C.-H. Lin is with the Institute of Communications Engineering, National Tsing-Hua University, Hsinchu, Taiwan. R.O.C. (e-mail: chihhsinglin@gmail. com). J.-S. Tsai is with the Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, R.O.C. (e-mail: thymine666@gmail.com). C.-T. Chiu is with the Department of Computer Science and Institute of Communications Engineering, National Tsing-Hua University, Hsinchu, Taiwan, R.O.C. (e-mail: ctchiu@cs.nthu.edu.tw). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIP.2010.2047906 disadvantage leads to the use of nonlinear filtering in image pro- cessing [29]–[31]. In this paper, we propose a filtering scheme that can remove both the additive Gaussian noise and the impulse noise. Addi- tive Gaussian noise is characterized by adding to each image pixel a value with a zero-mean Gaussian distribution [6], [27]. Such noise is usually introduced during image acquisition [27]. The zero-mean distribution property allows such noise to be removed by averaging pixel values locally [27]. Traditional linear filters can remove noise effectively but with the side effect of blurring edges and details significantly [6], [31], [32]. The more advanced methods for noise removal aim at preserving edges and details in images while removing the noise [25], [32]. Tomasi and Manduchi propose a bilateral filter that uses weights based upon spatial and radiometric similarity [1]. The bilateral filter has good results in removing noise while preserving edges in images [1], [32]. In addition, this method is noniterative, local and simple [1], [32]. Impulse noise is characterized by replacing a portion of an image pixels with noise values, leaving the remainder un- changed [29]. Such noise is introduced due to acquisition or transmission errors [27], [29], [30]. Nonlinear filters have been developed for removing impulse noise such as the traditional median filter [29]. Extensions of the median filter [2], [5], [14]–[21], [23], [24], [26] are proposed to meet various criteria, e.g., robustness, preservation of edge, or preservation of details. The learning algorithm [7] and the switching noise filters [14], [16], [17], [19], [22]–[24], [26] are also proposed. The genetic programming (GP) filter [7] is based upon the learning algorithm that is used to build two detectors, and this method requires a training procedure to arrive at an optimal classifi- cation based upon the measure of pixels and their neighbors. Methods that require training are less easily controlled and more unpredictable than traditional methods. The switching scheme detects impulse noise pixels before filtering and re- places them with estimated values while leaving the remaining pixels unchanged [14], [16], [17], [19], [22]–[24], [26]. Filters that can remove Gaussian or impulse noise, or any mixture thereof, have also been proposed [3], [4], [6], [8]–[11], [13]. The median-based signal-dependent rank ordered mean (SDROM) filter can remove impulse noise rather effectively, but when applied to images with Gaussian or mixed noise, it often produces a visually disappointing output [8]. This is because the rank-ordered mean gets corrupted in a high noise inten- sity window. Another median-based filter, the adaptive center- weighted median filter (ACWMF), uses a comparison of the center weighted medians and adaptive thresholds for detection [9]. When applied to Gaussian or mixed noise images, it cre- ates blur and removes the details. The directional weighted me- 1057-7149/$26.00 © 2010 IEEE