Bilateral filter

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

Filtering artifacts in images with 3D spatio-temporal fuzzy filters

Abstract: A method filters pixels in a sequence of images. Each image in the sequence is partitioned into blocks of pixels, and the images are processed sequentially. The energy is determined for each block of pixels in each image. The energy of each block is based on variances of intensities of the pixels in the sequence of images. A 3D fuzzy filter is applied to each current pixel in each current block during the sequential processing. The 3D fuzzy filter considers the energy of the block, and the intensities of pixels spatially adjacent and temporally adjacent to the current pixel to remove blocking and ringing artifacts.

Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach

Abstract: The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.

Reconstruction with 3D geometric bilateral filter

Abstract: In recent years, reverse engineering (RE) techniques have been developed for surface reconstruction from 3D scanned data. Typical sampling data, however, usually is large scale and contains unorganized points, thus leading to some anomalies in the reconstructed object. To improve performance and reduce processing time, Hierarchical Space Decomposition (HSD) methods can be applied. These methods are based on reducing the sampled data by replacing a set of original points in each voxel by a representative point, which is later connected in a mesh structure. This operation is analogous to smoothing with a simple low- pass filter (LPF). Unfortunately, this principle also smoothes sharp geometrical features, an effect that is not desired. The high performance results of bilateral filtering for removing noise from 2D images while preserving details motivated us to extend this filtering and apply it to 3D scan points. This paper introduces anisotropic 3D scan point filtering, which we have defined as 3D Geometric Bilateral Filtering (GBF). The proposed GBF method smoothes low curvature regions while preserving sharp geometric features, and it is robust, simple and fast.

FPGA (field programmable gate array)-based infrared image detail enhancing system and method

Abstract: The invention discloses an FPGA (field programmable gate array)-based infrared image detail enhancing system and method. The system comprises a bilateral filtering module, a gaussian filtering module, a histogram projecting module and an automatic gain control module, wherein the bilateral filtering module is connected with the gaussian filtering module which is connected with the histogram projecting module and the automatic gain control module respectively, and original input data firstly passes through the bilateral filtering module to obtain image pattern fundamental frequency information; the fundamental frequency information passes through the gaussian filtering module to be smoothened, and differencing is carried out between a result and the original input data so as to obtain image detail information; and the detail information is amplified by the automatic gain control module, the fundamental frequency information is compressed by the histogram projecting module, and the detail information and the fundamental frequency information are summed to obtain an output image. According to the invention, the contrast ratio of the image can be improved, the detail information can be enhanced, background noise can be restrained, and the common problems that the edge is fuzzy and a visual effect is poor in the image of a thermal infrared imager imaging system in the prior art can be solved.

SVM for edge-preserving filtering

Abstract: In this paper, we propose a new method to construct an edge-preserving filter which has very similar response to the bilateral filter. The bilateral filter is a normalized convolution in which the weighting for each pixel is determined by the spatial distance from the center pixel and its relative difference in intensity range. The spatial and range weighting functions are typically Gaussian in the literature. In this paper, we cast the filtering problem as a vector-mapping approximation and solve it using a support vector machine (SVM). Each pixel will be represented as a feature vector comprising of the exponentiation of the pixel intensity, the corresponding spatial filtered response, and their products. The mapping function is learned via ∊-SVM regression using the feature vectors and the corresponding bilateral filtered values from the training image. The major computation involved is the computation of the spatial filtered responses of the exponentiation of the original image which is invariant to the filter size given that an IIR O(1) solution is available for the spatial filtering kernel. To our knowledge, this is the first learning-based O(1) bilateral filtering method. Unlike previous O(1) methods, our method is valid for both low and high range variance Gaussian and the computational complexity is independent of the range variance value. Our method is also the fastest O(1) bilateral filtering yet developed. Besides, our method allows varying range variance values, based on which we propose a new bilateral filtering method avoiding the over-smoothing or under-smoothing artifacts in traditional bilateral filter.