Bilateral filter

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

Spatio-Temporal Photon Density Estimation Using Bilateral Filtering

Abstract: Photon tracing and density estimation are well established techniques in global illumination computation and rendering of high-quality animation sequences. Using traditional density estimation techniques it is difficult to remove stochastic noise inherent for photon-based methods while avoiding overblurring lighting details. In this paper we investigate the use of bilateral filtering for lighting reconstruction based on the local density of photon hit points. Bilateral filtering is applied in spatio-temporal domain and provides control over the level-of-details in reconstructed lighting. All changes of lighting below this level are treated as stochastic noise and are suppressed. Bilateral filtering proves to be efficient in preserving sharp features in lighting which is in particular important for high-quality caustic reconstruction. Also, flickering between subsequent animation frames is substantially reduced due to extending bilateral filtering into temporal domain

Method for filtering of images with bilateral filters and integral histograms

Abstract: The current invention describes a method for filtering an input image with a bilateral filter to produce an output image. The bilateral filter includes a spatial filter and a range filter. The method comprising the steps of: constructing an integral histogram from an input image including pixels, and wherein each pixel has an intensity; applying, for each pixel, the spatial filter to the integral histogram to produce a local histogram, each local histogram having a bin for a specified range of intensities of the pixels, each bin associated with a coefficient indicating a number of pixels in the specified range and an index to the coefficient; subtracting, for each bin in each local histogram, an intensity of the pixel from each index of the bin to produce a difference value; applying, for each bin, the range filter to each difference value to produce a response; scaling, each response by the corresponding coefficient to produce a scaled response; summing, for each local histogram, the scaled responses to produce a local response for the local histogram; summing, for each local histogram, the coefficients to produce a sum of the coefficient; and dividing, for each pixel, the local response by the sum of the coefficients to produce a response for the bilateral filter, which forms an output image.

Deblocking filtering processor and deblocking filtering method

Abstract: A deblocking filtering processor includes a first deblocking filtering section provided with a typical filter intensity calculation section and a typical filter section. The typical filter intensity calculation section selects a predetermined pixel line among a plurality of pixel lines crossing a block boundary as a typical pixel line for decoded image data of a moving image that are coded in units of blocks, and performs a filter intensity calculation to obtain a typical filter intensity on the basis of pixel values included in the typical pixel line. The typical filter section provides smoothing for pixel values included in the plurality of pixel lines on the basis of the typical filter intensity.

Method and apparatus for interpolating image information

Abstract: A method and apparatus for interpolating image information obtains pixel information for a plurality of pixels surrounding a location of a pixel to be interpolated, whether the pixel is a missing pixel or an existing pixel whose color or intensity information is to be changed, and applies a gradient square tensor operation on a plurality of surrounding pixels to determine if the pixel to be interpolated is part of a geometric feature. If it is determined that the interpolated pixel is part of a geometric feature, such as a diagonal line or other suitable geometric feature, the method and apparatus uses pixel information from at least some of the surrounding pixels that are also determined to be the in geometric feature. This may be performed on a group of pixel basis and may include, for example, utilizing a block or kernel of pixels and a moving window of blocks of pixels to utilize the plurality of surrounding pixels.