scispace - formally typeset
Search or ask a question
Topic

Bilateral filter

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


Papers
More filters
Journal ArticleDOI
TL;DR: An adaptive bilateral method for the smoothing of a point-sampled surface that overcomes the feature shrinking/shriveling problem and does not require surface triangulation or curvature computation, and is very suitable for the object of a pointed surface with high-curvature feature or sharp features.
Abstract: Optical inspection of blades is important in computer vision and manufacturing automation. One problem commonly encountered is that the scanned point cloud may be polluted by noise, mainly from the scanning equipment. How to smooth the blade surface while preserving the thin-walled feature of leading/trailing edges is a challenging task. In this paper, we propose an adaptive bilateral method for the smoothing of a point-sampled surface. This paper is motivated by a bilateral filtering technique of a two-dimensional image. The basis of the method is the application of information entropy to distinguish density difference of point cloud. By minimizing defined smoothing density entropy and preserving density entropy, the optimal surface-smoothing factor and feature-preserving factor are calculated at each vertex. Applying the obtained factors, the objective of smoothing surface while preserving thin-walled feature is achieved, which overcomes the feature shrinking/shriveling problem. Our method does not require surface triangulation or curvature computation, and is very suitable for the object of a point-sampled surface with high-curvature feature or sharp features. Its robustness and efficiency are confirmed by experiments.

12 citations

Proceedings ArticleDOI
12 Jun 2016
TL;DR: In this article, an approximation of the Gaussian bilateral filter is presented, whereby the number of operations is reduced to O(1) per pixel for any arbitrary σ s, and yet achieves very high quality filtering that is almost indistinguishable from the output of the original filter.
Abstract: The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires O(σ s 2) operations per pixel, where σ s is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to O(1) per pixel for any arbitrary σ s , and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.

12 citations

Journal ArticleDOI
TL;DR: This paper provides a fusion technique for multi-focus imaging using cross bilateral filter and non-subsampled contourlet transform and shows the significance of proposed scheme in comparison to state of art fusion schemes.
Abstract: This paper provides a fusion technique for multi-focus imaging using cross bilateral filter and non-subsampled contourlet transform. The snapshots are decomposed into distinct approximation and detail components. The original image and approximation component is passed through cross bilateral filter to obtain approximation weight map. Whereas, the detail components are combined using weighted average to obtain detail weight map. The weights are combined together and used with original images to obtain the resultant fused image. Visual and quantitative analysis shows the significance of proposed scheme in comparison to state of art fusion schemes.

12 citations

Proceedings ArticleDOI
01 Sep 2011
TL;DR: Experimental results indicate that the algorithm is effective for low illumination compensation, colour restoration and noise reduction, and a saturation enhancement function was proposed to ensure more natural colours.
Abstract: This article describes a novel framework for low-light colour image enhancement and denoising. To avoid influences from different colour channels, noise reduction and brightness/contrast enhancement are performed in different colour spaces. In the HSI space, the Bilateral filter is used for illumination- and reflection-component separation, and is effective for edge-preservation, halo removal and noise suppression. Brightness/contrast are extrapolated by using a newly designed histogram, where a suppression term based on the statistics of mathematical expectation and standard deviation was added to improve the algorithm's adaptability. Meanwhile, a saturation enhancement function was proposed to ensure more natural colours. In the YCbCr space, based on noise characteristics in low-light images, Gaussian and Median filters were adopted to reduce the noise. Experimental results indicate that the algorithm is effective for low illumination compensation, colour restoration and noise reduction.

12 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a new image denoising algorithm when the data is contaminated by a Poisson noise, which is based on a weighted linear combination of the observed image.
Abstract: We propose a new image denoising algorithm when the data is contaminated by a Poisson noise As in the Non-Local Means filter, the proposed algorithm is based on a weighted linear combination of the observed image But in contrast to the latter where the weights are defined by a Gaussian kernel, we propose to choose them in an optimal way First some "oracle" weights are defined by minimizing a very tight upper bound of the Mean Square Error For a practical application the weights are estimated from the observed image We prove that the proposed filter converges at the usual optimal rate to the true image Simulation results are presented to compare the performance of the presented filter with conventional filtering methods

12 citations


Network Information
Related Topics (5)
Image processing
229.9K papers, 3.5M citations
87% related
Image segmentation
79.6K papers, 1.8M citations
87% related
Feature (computer vision)
128.2K papers, 1.7M citations
86% related
Feature extraction
111.8K papers, 2.1M citations
86% related
Pixel
136.5K papers, 1.5M citations
84% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202257
2021116
2020145
2019203
2018204