Bilateral filter

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

Fast O(1) bilateral filtering using trigonometric range kernels

Abstract: It is well-known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not depend on the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image processing literature. Along with the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process non-linear and computationally intensive, especially when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant-time, by using trigonometric range kernels. This is done by generalizing the idea in [Porikli2008] of using polynomial range kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained in [Porikli2008] using polynomials.

Image processing system and method

Abstract: A method dividing an image into plural superpixels of plural pixels of the image. The method calculates an initial set of weights from a measure of similarity between pairs of pixels, from which a resultant set of weights is calculated for pairs of pixels that are less that a threshold distance apart on the image. The calculation calculates a weight for a pair of pixels as the sum over a set of third pixels of the product of initial weight of the first pixel of the pair of pixel with the third pixel and the weight of the third pixel with the second pixel. Each weight is then subjected to a power coefficient operation. The resultant set of weights and the initial set of weights are then compared to check for convergence. If the weights converge, the converged set of weights is used to divide the image into superpixels.

A Reconfigurable and Scalable FPGA Architecture for Bilateral Filtering

Abstract: Bilateral filter is an edge-preserving smoother that has applications in image processing, computer vision, and computational photography. In the past, field-programmable gate array (FPGA) implementations of the filter have been proposed that can achieve high throughput using parallelization and pipelining. An inherent limitation with direct implementations is that their complexity scales as $O(\omega ^2)$ with the filter width $\omega$ . In this paper, we propose an FPGA implementation of a fast bilateral filter that requires just $O(1)$ operations for any arbitrary $\omega$ . The attractive feature of the FPGA implementation is that it is both scalable and reconfigurable. To the best of our knowledge, this is the first scalable FPGA implementation of the bilateral filter. As an application, we use the FPGA implementation for image denoising.

Improved bilateral filtering for SAR image despeckling

Abstract: To effectively preserve edges of synthetic aperture radar (SAR) images while cleanly despeckling, an improved bilateral filtering algorithm (IBF) is proposed. A threshold value is first calculated, which is used to distinguish whether the current pixel and its neighbouring pixels are homogeneous or not. Then, bilateral filtering (BF) is improved by reducing its two parameters to one, which emphasises the contribution of adjacent homogeneous pixels to the approximation of the current pixel. Finally, IBF is applied to remove speckles. The despeckling results for SAR images show that the visual quality and evaluation indexes of IBF outperform those of enhanced Lee filtering (ELF) and BF.

Removing snow from an image via image decomposition

Abstract: Snowfall removal from an image is a challenging problem. In this paper, we propose a snowfall removal framework via image decomposition based on Morphological component analysis. The proposed methods first decompose an image into low frequency (LF) and high frequency (HF) parts using bilateral filter. The high frequency part is then decomposing into “snow component” and “non snow component” by performing dictionary learning and sparse coding.