Bilateral filter

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

Adaptive strategy for demosaicing microgrid polarimeter imagery

Abstract: Microgrid imaging polarimeters consist of a focal plane array sensor with linear polarization filters of differing orientations overlaid at each pixel, similar in concept to the arrangement of spectral filters in a color CCD Bayer pattern camera However, unlike spectral color cameras, microgrid systems use polarimetrically modulated intensity measurements to reconstruct the Stokes vector at each point in an imaged scene Stokes reconstruction of imagery from these devices has traditionally been performed using linear filtering techniques While linear filtering strategies can yield reasonable estimates of the Stokes imagery, the filtering often results in loss of high frequency content in addition to introducing typical demosaicing artifacts (such as aliasing and zippering effects) Here we develop an adaptive demosaicing strategy based upon the concept of bilateral filtering as a means for reducing interpolation artifacts while preserving high frequency image content often removed by non-adaptive linear interpolators We demonstrate the demosaicing strategy and compare it against imagery estimated using other techniques on LWIR microgrid data1,2

Depth Image Super Resolution Based on Edge-Guided Method

Abstract: Depth image super-resolution (SR) is a technique which can reconstruct a high-resolution (HR) depth image from a low-resolution (LR) depth image. Its purpose is to obtain HR details to meet the needs of various applications in computer vision. In general, conventional depth image SR methods often cause edges in the final HR image to be blurred or ragged. To solve this problem, an edge-guided method for depth image SR is presented in this paper. To get high-quality edge information, a pair of sparse dictionaries was applied to reconstruct edges of depth image. Then, with the guidance of these high-quality edges, a depth image was interpolated by using a modified joint bilateral filter. Edge-guided method can preserve the sharpness of edges and effectively avoid generating blurry and ragged edges when SR is performed. Experiments showed that the proposed method can get better results on both subjective and objective evaluation, and the reconstructed performance was superior to conventional depth image SR methods.

Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data

Abstract: Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.