One of tasks of image filter is to preserve strong edge structure and smooth textures in the given image. Recently, many approaches have been proposed to accomplish this challenging task. In this paper, we propose a scale adaptive structure tensor based rolling trilateral filter to smooth detailed small textures while preserving prominent structures. The proposed method first estimates scale at each pixel using a structure measure; then computes the eigenvalues and eigenvectors of structure tensor constructed from gradient at each pixel. The trilateral filter includes anisotropic weight in spatial space, the Gaussian weight in range space and the Gaussian weight of inner product of eigenvectors in gradient space. Results of experiments conducted on many natural images demonstrate that the proposed filter performs well.
Recently a filtering method based on side windows has been reported. Although this method has strong ability to preserve edge structure, its ability to smooth textures is weaker than using a full window. In this paper, we propose a scale adaptive side window based bilateral filter (SASWBF). An expanding ratio parameter is designed to control the side window varying to the full window. To adapt the size of spatial kernel at each pixel, the scale of filtering window is estimated using a structure measure. A bilateral filter using Fourier basis is adopted to accelerate the filter processing. The computational complexity of the proposed smoothing filter does not depend on the window size and thus is in almost constant time. Our experimental results demonstrates that the proposed filter performs well.
Due to its simplicity, median filter is a very famous and useful tool in the fields such as image processing and computer graphics. Median filter is mainly for eliminating irrelevant details, especially removing salt-and- pepper noises in image. It has the ability to preserve structural edges compared with box filter and Gaussian filter, however, this ability is very limited. When the radius of filter window becomes larger, the edge-preserving ability also becomes very weak. In this paper, we propose a median-like filter that removes small details including salt-and-pepper noises in image while having stronger edge-preserving ability than classical median filter. The filter computes the output at the observed pixel using 8 sub-windows and a full window. Among these windows, 4 of them are built on the 4 quadrants respectively, other 4 of them are on left, right, top, and bottom half planes. All of these sub-windows contain the observed pixel. Moreover, since medians are computed from histograms, we update column histograms and kernel histograms by simple subtraction and addition operations to accelerate the filtering. The computational complexity of the proposed median-like filter is independent of window size and thus is in constant time. A SSIM (Structural SIMilarity) evaluation demonstrates that the proposed median-like filter performs well.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.