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 published on a yearly basis
Papers
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TL;DR: This letter proposes to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection, and compares with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods.
Abstract: An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance.
57 citations
Patent•
03 Aug 2000
TL;DR: In this paper, an image filter approximating the envelope of the organized storm radar image is applied to a pixel in a received weather radar image to generate a processed pixel value and a variability value is determined from the variation in the pixel values of the neighboring pixels which lie within the image filter.
Abstract: A method and apparatus for determining the predictability of an element in a weather radar image. An image filter approximating the envelope of the organized storm radar image is applied to a pixel in a received weather radar image to generate a processed pixel value. A variability value is determined from the variation in the pixel values of the neighboring pixels which lie within the image filter. The predictability is generated from the processed pixel value and the variability. Pixels having high processed pixel values and low variabilities typically correspond to pixels within a strong organized storm and, therefore, are more predictable. Pixels having low processed pixel values and high variabilities, such as pixels representative of airmass storms, generally have lower predictabilities.
56 citations
23 Aug 2010
TL;DR: Improve depth quality by performing a newly-designed joint bilateral filtering, color segmentation-based boundary refinement, and motion estimation-based temporal consistency to enhance depth images captured by a time-of-flight depth sensor spatially and temporally.
Abstract: In this paper, we present a new method to enhance depth images captured by a time-of-flight (TOF) depth sensor spatially and temporally. In practice, depth images obtained from TOF depth sensors have critical problems, such as optical noise existence, unmatched boundaries, and temporal inconsistency. In this work, we improve depth quality by performing a newly-designed joint bilateral filtering, color segmentation-based boundary refinement, and motion estimation-based temporal consistency. Experimental results show that the proposed method significantly minimizes the inherent problems of the depth images so that we can use them to generate a dynamic and realistic 3D scene.
56 citations
TL;DR: Novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented, which yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.
Abstract: Depth maps taken by the low cost Kinect sensor are often noisy and incomplete. Thus, post-processing for obtaining reliable depth maps is necessary for advanced image and video applications such as object recognition and multi-view rendering. In this paper, we propose adaptive directional filters that fill the holes and suppress the noise in depth maps. Specifically, novel filters whose window shapes are adaptively adjusted based on the edge direction of the color image are presented. Experimental results show that our method yields higher quality filtered depth maps than other existing methods, especially at the edge boundaries.
56 citations
TL;DR: The proposed image denoising framework mainly consists of an impulse noise detector (IND), an edge connection precedure and an adaptive bilateral filter (ABF), which switches between Gaussian and impulse noise depending on the impulse noise detection results.
56 citations