Bilateral filter

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

Real-time stereo using approximated joint bilateral filtering and dynamic programming

Abstract: We present a stereo algorithm that is capable of estimating scene depth information with high accuracy and in real time. The key idea is to employ an adaptive cost-volume filtering stage in a dynamic programming optimization framework. The per-pixel matching costs are aggregated via a separable implementation of the bilateral filtering technique. Our separable approximation offers comparable edge-preserving filtering capability and leads to a significant reduction in computational complexity compared to the traditional 2D filter. This cost aggregation step resolves the disparity inconsistency between scanlines, which are the typical problem for conventional dynamic programming based stereo approaches. Our algorithm is driven by two design goals: real-time performance and high accuracy depth estimation. For computational efficiency, we utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this aggregation process over two orders of magnitude. Over 90 million disparity evaluations per second [the number of disparity evaluations per seconds (MDE/s) corresponds to the product of the number of pixels and the disparity range and the obtained frame rate and, therefore, captures the performance of a stereo algorithm in a single number] are achieved in our current implementation. In terms of quality, quantitative evaluation using data sets with ground truth disparities shows that our approach is one of the state-of-the-art real-time stereo algorithms.

Method and apparatus for generating a dense depth map using an adaptive joint bilateral filter

Abstract: A method and apparatus for generating a dense depth map. In one embodiment, the method includes applying a joint bilateral filter to a first depth map to generate a second depth map, where at least one filter weight of the joint bilateral filter is adapted based upon content of an image represented by the first depth map, and the second depth map has a higher resolution than the first depth map.

Imaging apparatus, noise reduction apparatus, noise reduction method, and noise reduction program

Abstract: Noise reduction is performed on the basis of characteristics of an image in a detection range. A noise reduction block 4′ performs a second-order differentiation process and a symmetry process to decide adjacent pixels with which noise reduction is preformed for an attention pixel. With the pixel level of the attention pixel in the detection range and the pixel levels of adjacent pixels used for noise reduction, an arithmetic mean processing section 16 calculates a mean value. A median filter 17 selects a median value. With the number of pixels used for noise reduction, it is determined whether the image in the detection range contains a flat portion, a ramp portion, or an edge. The mean value and the median value are weight-added with a weighted coefficient that are changed on the basis of characteristics of the image. The result is substituted for the level of the attention pixel. When the attention pixel is an isolated point, an all-pixel median filter section 31 selects a medium value of the levels of all the pixels in the detection range including the attention pixel and substitutes the median value for the level of the attention pixel.