Bilateral filter

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

Image compression technique

Abstract: An optical and electronic scan produces an image composed of binary value pixels. An automatic picture compression routine reduces the number of pixels in the image. A first stage four to one reduction in the number of pixels is achieved by treating each mutually exclusive four pixel unit as a separate set. A binary value is assigned to each four pixel set. That binary value is assigned to a single pixel in a first output image. The same process is repeated a second time to provide a final output image that has one-sixteenth the number of pixels as has the original input image. The value assigned to each four pixel unit set is a weighted function of the binary value of each of the sixteen pixels in a four pixel by four pixel subfield in which the unit set is centered. The greatest weight is give to the center pixels, that is, to the four pixels of the unit set. Lesser weight is given to the peripheral pixels. Among the peripheral pixels, lesser weight is given to the four corner pixels than is given to the eight side pixels between the corners. In the weighting process, the significance of the binary value of the sixteen pixels in the subfield is in part a function of the total pattern of the pixel values in the subfield.

Fast Scale-Adaptive Bilateral Texture Smoothing

Abstract: In the classical bilateral filter, a range kernel is used together with a spatial kernel for smoothing out fine details while simultaneously preserving edges. More recently, it has been demonstrated that even coarse textures can be smoothed using joint bilateral filtering. In this paper, we demonstrate that the superior texture filtering results can be obtained by adapting the spatial kernel at each pixel. To the best of our knowledge, spatial adaptation (of the bilateral filter) has not been explored for texture smoothing. The rationale behind adapting the spatial kernel is that one cannot smooth beyond a certain level using a fixed spatial kernel, no matter how we manipulate the range kernel. In fact, we should simply aggregate more pixels using a sufficiently wide spatial kernel to locally enhance the smoothing. Based on this reasoning, we propose to use the classical bilateral filter for texture smoothing, where we adapt the width of the spatial kernel at each pixel. We describe a simple and efficient gradient-based rule for the latter task. The attractive aspect is that we are able to develop a fast algorithm that can accelerate the computations by an order without visibly compromising the filtering quality. We demonstrate that our method outperforms classical bilateral filtering, joint bilateral filtering, and other filtering methods, and is competitive with the optimization methods. We also present some applications of texture smoothing using the proposed method.

Learning-Based Artifact Removal via Image Decomposition for Low-Dose CT Image Processing

Abstract: Streak artifacts and mottle noise often appear in low-dose CT (LDCT) images due to excessive quantum noise in low-dose X-ray imaging process, thus degrading CT image quality. This research is aimed at improving the quality of LDCT images via image decomposition and dictionary learning. The proposed method first decomposes a LDCT image into the low-frequency (LF) and high-frequency (HF) parts by a bilateral filter. The HF part is then decomposed into an artifact component and a tissue component by performing dictionary learning (DL) and sparse coding. The tissue component is combined with the LF part to obtain the artifact-suppressed image. At last, a DL method is applied to further reduce the residual artifacts and noise. Different from previous research works with sparse representation, the proposed method does not need to collect training images in advance. The results of numerical simulation and clinical data experiments indicate the effectiveness of the proposed approach.

Dynamic Positron Emission Tomography Image Restoration via a Kinetics-Induced Bilateral Filter

Abstract: Dynamic positron emission tomography (PET) imaging is a powerful tool that provides useful quantitative information on physiological and biochemical processes. However, low signal-to-noise ratio in short dynamic frames makes accurate kinetic parameter estimation from noisy voxel-wise time activity curves (TAC) a challenging task. To address this problem, several spatial filters have been investigated to reduce the noise of each frame with noticeable gains. These filters include the Gaussian filter, bilateral filter, and wavelet-based filter. These filters usually consider only the local properties of each frame without exploring potential kinetic information from entire frames. Thus, in this work, to improve PET parametric imaging accuracy, we present a kinetics-induced bilateral filter (KIBF) to reduce the noise of dynamic image frames by incorporating the similarity between the voxel-wise TACs using the framework of bilateral filter. The aim of the proposed KIBF algorithm is to reduce the noise in homogeneous areas while preserving the distinct kinetics of regions of interest. Experimental results on digital brain phantom and in vivo rat study with typical (18)F-FDG kinetics have shown that the present KIBF algorithm can achieve notable gains over other existing algorithms in terms of quantitative accuracy measures and visual inspection.

Constant-Time Filtering Using Shiftable Kernels

Abstract: It was recently demonstrated in that the nonlinear bilateral filter can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in can be extended to few other linear and nonlinear filters . While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in , we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum , , and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (nonlinearshiftable) Gaussian kernel that is ubiquitously used in image filtering.