Bilateral Filtering

Although three-dimensional (3D) displays enhance visual quality more than two-dimensional (2D) displays do, the depth information required for 3D displays is unavailable in the conventional 2D content. Therefore, converting 2D videos into 3D ones has become an important issue in emerging 3D applications. This work presents a novel algorithm that automatically converts 2D videos into 3D ones. The proposed algorithm utilizes the edge information to segment the image into object groups. A depth map is then assigned based on a hypothesized depth gradient model. Next, the depth map is block-based assigned by cooperating with a cross bilateral filter to generate visually comfortable depth maps efficiently and also diminish the block artifacts. A multiview video can be readily generated by using a depth image-based rendering method.

The object-based methodology is one of the most commonly used strategies for processing high spatial resolution images. A prerequisite to object-based image analysis is image segmentation, which is normally defined as the subdivision of an image into separated regions. This study proposes a new image segmentation methodology based on a self-calibrating multi-band region growing approach. Two multispectral aerial images were used in this study. The unsupervised image segmentation approach begins with a first step based on a bidirectional filtering, in order to eliminate noise, smooth the initial image and preserve edges. The results are compared with ones obtained from Definiens Developper software.

We provide and evaluate a fusion algorithm of remotely sensed images, i.e. the fusion of a panchromatic (PAN) image with a multi-spectral (MS) image using bilateral filtering, applied to images of three different sensors: SPOT 5, Landsat ETM+ and Quickbird. To assess the fusion process, we use six quality indexes, that confirm, along with visual analysis, good overall results for the three sensors.

2D vessel segmentation algorithms working on 2D digital subtraction angiography (DSA) images suffer from inhomogeneous contrast agent distributions within the vessels. In this work, we present a novel semi-automatic vessel segmentation method based on local adaptive contrast enhancement. Either a forward projected 3D centerline or a set of manual selected seed points define the vessel branches to be segmented on the image. The algorithm uses bilateral filtering followed by local contrast enhancement to eliminate intensity inhomogeneity within the vessel region that is caused by unequally distributed contrast agent. Our segmentation algorithm is extensively evaluated on 45 different DSA images and exhibits an average Hausdorff distance of 22 pixels and sensitivity of 89 %.

Abstract. Recently, an energy-based unified framework for image de-noising was proposed by Mrázek et al. [10], from which existing nonlinear filters such as M-smoothers, bilateral filtering, diffusion filtering and reg-ularisation approaches, are obtained as special cases. ...

Clinical magnetic resonance imaging (MRI) data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to increase the : Signal-to-Noise Ratio (SNR) and improve image quality. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. In this paper we propose an extension of the bilateral filter: multi resolution bilateral filter (MRBF), with wavelet transform (WT) sub-bands mixing. The proposed wavelet sub-bands mixing is based on a multi resolution approach for improving the quality of image denoising filter, which turns out to be very effective in eliminating noise in noisy images. Quantitative validation was carried out on synthetic datasets generated with the Brain Web simulator. Comparison with other methods, such as nonlinear diffusion, Fourth-Order Partial Differential Equations, Total variation, Nonlocal mean, Wavelet thresholding, and Bilateral filters, shows that the proposed multi resolution bilateral filter (MRBF) produces better denoising results. The mathematical analysis is based on the analysis of the "method noise", defined as the difference between a digital image and its denoised version. The MRBF algorithm is also proven to be asymptotically optimal under a generic statistical image model. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

Filtering has been a major approach used in the homogenization-based methods for structural topology optimization to suppress the checkerboard pattern and relieve the numerical instabilities. In this paper a bilateral filtering technique originally developed in image processing is presented as an efficient approach to regularizing the topology optimization problem. A non-linear bilateral filtering process leads to a suitable problem regularization to eliminate the checkerboard instability, pronounced edge preserving smoothing characteristics to favour the 0–1 convergence of the mass distribution, and computational efficiency due to its single pass and non-iterative nature. Thus, we show that the application of the bilateral filtering brings more desirable effects of checkerboard-free, mesh independence, crisp boundary, computational efficiency and conceptual simplicity. The proposed bilateral technique has a close relationship with the conventional domain filtering and range filtering. The proposed method is implemented in the framework of a power-law approach based on the optimality criteria and illustrated with 2D examples of minimum compliance design that has been extensively studied in the recent literature of topology optimization and its efficiency and accuracy are highlighted. Copyright © 2005 John Wiley & Sons, Ltd.

Like bilateral filter (BF), cross bilateral filter (CBF) considers both gray-level similarities and geometric closeness of the neighboring pixels without smoothing edges, but it uses one image for finding the kernel and other to filter, and vice versa. In this paper, it is proposed to fuse source images by weighted average using the weights computed from the detail images that