Bilateral filter

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

Constant Time Joint Bilateral Filtering Using Joint Integral Histograms

Abstract: In this brief, we present a constant time method for the joint bilateral filtering. First, we propose an image data structure, coined as joint integral histograms (JIHs). Extending the classic integral images and the integral histograms, it represents the global information of two correlated images. In a JIH, the value at each bin indicates an integral determined by the two images. Then, the joint bilateral filtering is transformed to computation and manipulation of histograms. Utilizing the JIHs, we are capable of joint bilateral filtering in constant time. Its performance is validated in a digital photography approach using Flash–noFlash image pairs. Compared with the brute-force method, the proposed method achieves a speedup factor of 2–3 orders of magnitude while producing similar filtering results.

Static/Dynamic Filtering for Mesh Geometry

Abstract: The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering.

Super-resolution device and method

Abstract: A super-resolution device and method for setting at least one of a plurality of pixels included in image data as target pixels, the image data including pixels arranged in a screen and pixel values representing brightness, an area including the target pixel and peripheral pixels as a target area, and an area for searching pixel value change patterns in the target pixel area; calculating a difference between a first change pattern and second change pattern; comparing a difference between the first and second change patterns; calculating a pixel value of a super-resolution image having a number of pixels larger than a number of pixels included in the image data on the basis of a decimal-accuracy-vector, an extrapolated vector, and pixel values obtained from the image data.

A novel switching bilateral filtering algorithm for depth map

Abstract: In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.

A novel improved median filter for salt-and-pepper noise from highly corrupted images

Abstract: This paper proposes a novel improved median filter algorithm for the images highly corrupted with salt-and-pepper noise. Firstly all the pixels are classified into signal pixels and noisy pixels by using the Max-Min noise detector. The noisy pixels are then separated into three classes, which are low-density, moderate-density, and high-density noises, based on the local statistic information. Finally the weighted 8-neighborhood similarity function filter, the 5×5 median filter and the 4-neighborhood mean filter are adopted to remove the noises for the low, moderate and high level cases, respectively. In experiment, the proposed algorithm is compared with three typical methods, named Standard Median filter, Extremum Median filter and Adaptive Median filter, respectively. The validation results show that the proposed algorithm has better performance for capabilities of noise removal, adaptivity, and detail preservation, especially effective for the cases when the images are extremely highly corrupted.