Bilateral filter

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

Binocular stereoscopic vision matching method combining depth characteristics

Abstract: The invention discloses a binocular stereoscopic vision matching method combining depth characteristics The binocular stereoscopic vision matching method comprises: obtaining a depth characteristic pattern from left and right images through a convolutional neural network; calculating a truncation similarity measurement degree of pixel depth characteristics by taking the depth characteristics as the standard, and constructing a truncation matching cost function combining color, gradients and depth characteristics to obtain a matched cost volume; processing the matched cost volume by adopting a fixed window, a variable window and a self-adaptive weight polymerization or guide filtering method to obtain a cost volume polymerized by a matching cost; selecting an optimal parallax error of the cost volume by adopting WTA (Wireless Telephony Application) to obtain an initial parallax error pattern; then finding a shielding region by adopting a double-peak test, left-right consistency detection, sequence consistency detection or shielding constraint algorithm, and giving a shielding point to a parallax error value of a same-row point closest to the shielding point to obtain a parallax error pattern; and filtering the parallax error pattern by adopting a mean value or bilateral filter to obtain a final parallax error pattern By adopting the binocular stereoscopic vision matching method combining the depth characteristics, the incorrect matching rate of three-dimensional matching can be effectively reduced, the images are smooth and image edges including edges of small objects are effectively kept

Constant Time Stereo Matching

Abstract: Typically, local methods for stereo matching are fast but have relatively low degree of accuracy while global ones, though costly, achieve a higher degree of accuracy in retrieving disparity information. Recently, however, some local methods such as those based on segmentation or adaptive weights are suggested to possibly achieve more accuracy than global ones in retrieving disparity information. The problem for these newly suggested local methods is that they cannot be easily adopted since they may require more computational costs which increase in proportion to the window size they use. To reduce the computational costs, therefore, we propose in this paper the stereo matching method that use domain weight and range weight similar to those in the bilateral filter. Our proposed method shows constant time O(1) for the stereo matching. Our experiments spend constant time for computation regardless of the window size but our experimental results show that the accuracy of generated depth map is as good as the ones suggested by recent methods.

Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means

Abstract: We address the problem of sharpness enhancement of images. Existing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter suffer from halo effects, whereas techniques based on weighted least squares extract low contrast features as detail. Other techniques require multiple images and are not tolerant to noise.

Depth super resolution using bilateral filter

Abstract: Depth maps are used in many applications, e.g. 3D television, stereo matching, segmentation, etc. Recently, a new generation of active 3D range sensors, such as time-of-flight (TOF) cameras, enables recording of full frame depth maps at video frame rate. Unfortunately, depth maps captured with the TOF cameras have limited resolution and poor image quality, being serverely influenced by the random and systematic noise, which makes them innaposite for generating high quality 3D images. In this paper, we proposed a method to enhance the quality and increase the spatial resolution of range data by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive upsampling filter that takes into account the inherent noisy nature of depth data. Thus, we can improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry.

Using difference kernels for image filtering

Abstract: Methods and apparatus, including computer program products, for filtering an image. A filter kernel is received to determine one or more filtered values for each pixel in a sequence of pixels, where adjacent pixels are separated by a characteristic distance in the image. A difference kernel is defined based on local differences between a first kernel and a second kernel that are defined by the filter kernel centered at a first location and a second location, respectively. The second location is separated from the first location by the characteristic distance separating adjacent pixels in the sequence. The difference kernel is used to determine a difference between filtered values of adjacent pixels in the sequence. For depth of field filtering, the filter kernel can include a blur filter kernel that is based upon depth values of pixels in the sequence.