LBF:Learnable Bilateral Filter For Point Cloud Denoising
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Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual parameter adjustment; this inconvenience discounts the efficiency and user experience to obtain satisfied denoising results. We propose LBF, an end-to-end learnable bilateral filtering network for point cloud denoising; to our knowledge, this is the first time. Unlike the conventional BF and its variants that receive the same parameters for a whole point cloud, LBF learns adaptive parameters for each point according its geometric characteristic (e.g., corner, edge, plane), avoiding remnant noise, wrongly-removed geometric details, and distorted shapes. Besides the learnable paradigm of BF, we have two cores to facilitate LBF. First, different from the local BF, LBF possesses a global-scale feature perception ability by exploiting multi-scale patches of each point. Second, LBF formulates a geometry-aware bi-directional projection loss, leading the denoising results to being faithful to their underlying surfaces. Users can apply our LBF without any laborious parameter tuning to achieve the optimal denoising results. Experiments show clear improvements of LBF over its competitors on both synthetic and real-scanned datasets.Keywords:
Feature (linguistics)
Non-Local Means
In this paper the approach of denoising is solved by a new hybrid technique, which associates the different denoising methods. Wavelet threshold, bilateral filtering and median filtering are the three different filters employed in our hybrid technique. While doing image denoising, Spatial averaging and Edge preservation are the two different characteristics which will be considered and obviously each filter will have its own characteristics. The bilateral filter does the spatial averaging without smoothing the edges whereas a wavelet threshold filter removes the noise by removing the high frequency components with relatively lesser edge preservation. A median filter removes the noise with very good edge preservation. No filter gives the robust result in terms of both spatial averaging and edge preservation. Our new method proposes an adaptive combination of these three filtering methods, which outperforms the different denoising methods in terms of PSNR and edge preservation. Simulated experimental results were given for the proposed method.
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Edge-preserving smoothing
Non-Local Means
Video denoising
Step detection
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Non-Local Means
Video denoising
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The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.
Similarity (geometry)
Non-Local Means
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Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.
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The conventional denoising research mainly focuses on image filters,which can't relate the special relativity with the temporal relativity of videos.To take full advantage of spatial-temporal relations in denoising,A rigid model is estab-lished and the adaptive median filtering based on it is researched.The rigid model resolves videos into rigid block containing only parallel and whirl move,organizes the pixels with linear relations.Rigid Model Based Adaptive Median Filter(RBAMF)selects pixels neighbor to filtered pixels in both space and time domain in the rigid for median computation.The scale of ref-erenced pixels is decided by degree of contamination.Denoising experiment is performed with familiar test videos.Experiment Results prove that denoising based on rigid model is feasible.The denoising effect of RBAMF overmatches the effect of spe-cial adaptive median filter,which illustrates superiority of spatial-temporal denoising method.
Video denoising
Non-Local Means
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In the field of image analysis, denoising is an important preprocessing task. The design of an efficient, robust, and computationally effective edge preserving denoising algorithm is a widely studied, and yet unsolved problem. One of the most efficient edge-preserving denoising algorithms is the bilateral filter, which is an intuitive generalization of the local M-smoother. In this paper, we propose to modify both the bilateral filter and the local M-smoother to use patches of the image instead of single pixels in the denoising process. With this modification, the filtering effect becomes more sensitive to the different areas of the image and the filtering results improve. The denoising quality of these patch-based filters is evaluated on test images and compared to the classical bilateral filtering and local M-smoother.
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Video denoising
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This paper proposes a denoising algorithm which performs non-local means bilateral filtering. As existing literature suggests, non-local means (NLM) is one of the widely used denoising techniques, but has a critical drawback of smoothing of edges. In order to improve this, we perform fast and efficient NLM using Approximate Nearest Neighbour Fields and improve the edge content in denoising by formulating a joint-bilateral filter. Using the proposed joint bilateral, we are able to denoise smooth regions using the NLM approach and efficient edge reconstruction is obtained from the bilateral filter. Furthermore, to avoid tedious parameter selection, we carry out a noise estimation before performing joint bilateral filtering. The proposed approach is observed to perform well on high noise images.
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One of the bottleneck in medical imaging is due to low Signal-to-Noise Ratio(SNR) which requires long and repeated acquisition of the same subject to reduce noise and blur. To obtain a high SNR image without lengthy repeated scans, post processing of data such as denoising plays a critical role. Bilateral and Nonlocal means (NLm) filtering are commonly used procedures for medical image denoising. In this paper we propose a new thresholding scheme to wavelet and contour let based denoising by introducing a scaling factor to universal threshold. Also we propose the aforementioned novel contour let thresholding scheme as a preprocessing step on bilateral filtering and NLm denoising. Simulation results show that the proposed single entity comprising the novel preprocessing step and bilateral or NLm denoising is superior in terms of PSNR and perceptual quality compared to Bilateral filtering or NLm denoising used alone.
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Bilateral filter and Total variation image denoising are widely used in image denoising. In low noisy level, bilateral filtering is better than TV denoising for it reveals better SNR and sharper edges. However, in high noisy level, TV denoising outperforms bilateral filtering in terms of SNR and more details of non edges. It is very difficult to perform denoising of a very noisy image for the resulted image rarely improves its SNR comparing to the original noisy one. Even though Total variation image denoising could be used for a very noisy image, the resulted SNR still needs some improvement. In this research, the K-means-based Bilateral-TV denoising (K-BiTV) approach using pixel-wise bilateral filtering and TV denoising has been derived based on the gradient magnitude calculation of the guideline map using K-means clusters. The denoising result of K-BiTV was depended on the level of noise density and the appropriate cluster. The experimental result showed that comparing to the conventional TV denoising and bilateral filter, K-BiTV gave the higher SNRs for some images with higher level of noise density.
Video denoising
Non-Local Means
Total variation denoising
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