Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

Method and apparatus for bilateral high pass filter

Abstract: A target pixel and surrounding pixels corresponding to the target pixel are obtained from a digitally represented image. A bilateral high pass filtering kernel is determined based at least in part upon the target pixel and the surrounding pixels. A high pass spatial filtering kernel is provided and multiplied with the high pass photometric filtering kernel to provide a bilateral high pass filtering kernel. The resulting bilateral high pass filtering kernel is thereafter applied to the target pixel and the surrounding pixels to provide a filtered pixel. When it is desirable to combine noise filtering capabilities with sharpening capabilities, the bilateral high pass filter of the present invention may be combined with a bilateral low pass filtering kernel to provide a combined noise reduction and edge sharpening filter. The present invention may be advantageously applied to a variety of devices, including cellular telephones that employ image sensing technology.

Method of depth-based imaging using an automatic trilateral filter for 3d stereo imagers

Abstract: A system of stereo imagers, including image processing units and methods of blurring an image, is presented. The image is received from an image sensor. For each pixel of the image, a depth filter component is determined based on a focal area of the image and a depth map associated with the image. For each pixel of the image, a trilateral filter is generated that includes a spatial filter component, a range filter component and the depth filter component. The respective trilateral filter is applied to corresponding pixels of the image to blur the image outside of the focal area. A refocus area or position may be determined by imaging geometry or may be selected manually via a user interface.

A filtered backprojection algorithm with ray-by-ray noise weighting

Abstract: Purpose: This paper derives a ray-by-ray weighted filtered backprojection (rFBP) algorithm, based on our recently developed view-by-view weighted, filtered backprojection (vFBP) algorithm. Methods: The rFBP algorithm directly extends the vFBP algorithm by letting the noise weighting vary from channel to channel within each view. The projection data can be weighted in inverse proportion to their noise variances. Also, an edge-preserving bilateral filter is suggested to perform post filtering to further reduce the noise. The proposed algorithm has been implemented for the circular-orbit cone-beam geometry based on Feldkamp's algorithm. Results: Image reconstructions with computer simulations and clinical cadaver data are presented to illustrate the effectiveness and feasibility of the proposed algorithm. The new FBP-type algorithm is able to significantly reduce or remove the noise texture, which the conventional FBP is unable to do. The computation time of the proposed rFBP algorithm is approximately the same as the conventional FBP algorithm. Conclusions: A ray-based noise-weighting scheme is introduced to the FBP algorithm. This new FBP-type algorithm significantly reduces or removes the streaking artifacts in low-dose CT.

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

Abstract: We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.