Bilateral filter

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

Programmable and adaptive temporal filter for video encoding

Abstract: A technique is provided for programmably and adaptively temporally filtering pixel values of frames of a sequence of video frames. The technique includes determining a pixel value difference between a pixel of a current frame and a corresponding pixel of a temporally previous frame; and adaptively filtering the pixel of the current frame using a selected filter coefficient. The filter coefficient is selected employing the pixel value difference. For example, multiple thresholds could be employed to differentiate between multiple filter coefficients, with the pixel value difference being employed to determine which filter coefficient is selected for the adaptive filtering. The thresholds and the filter coefficients can also be programmable. Further, the temporal filter can be integrated with a repeat field detection unit of a motion video encoder in order to conserve memory bandwidth.

Motion detection via image alignment

Abstract: Pixels of an image are classified as being stationary or moving, based on the gradient of the image in the vicinity of each pixel The values of corresponding pixels in two sequential images are compared If the difference between the values is less than the image gradient about the pixel location, or less than a given threshold value above the image gradient, the pixel is classified as being stationary By classifying each pixel based on the image gradient in the vicinity of the pixel, the sensitivity of the motion detection classification is reduced at the edges of objects, and other regions of contrast in an image, thereby minimizing the occurrences of ghost artifacts caused by the misclassification of stationary pixels as moving pixels

Method and apparatus for compressing and decompressing a video image

Abstract: Edge or contour information is extracted from an image array by filtering and encoded. In order to improve reproduction accuracy, two separate filters (302, 304) are used to detect edge information. One of the filters detects 'sharp' edges, or edges in which the discontinuity in pixel intensity values occurs over a range of a few pixels. The other filter detects 'level' edges in which the pixel intensity value discontinuity occurs over a larger range of pixels than the 'sharp' edges. The 'smooth' areas between edges or contours are assumed to vary continuously between the contours, but for efficient implementation, a one-dimensional linear interpolation (216) is used to regenerate the contour information between edges. In addition, to further improve performance, a line is fitted to the pixel intensity data. The end values of this line are then used for the pixel intensity data. In order to still further improve performance, the pixel intensity values associated with each contour are divided into groups and each group is then encoded. Another improvement is accomplished by mean coding the residual error.

Single image fog removal using bilateral filter

Abstract: A novel and efficient fog removal algorithm is proposed. Fog formation is due to attenuation and airlight. Attenuation reduces the contrast and airlight increases the whiteness in the scene. Proposed algorithm uses bilateral filter for the estimation of airlight and recover scene contrast. Qualitative and quantitative analysis demonstrate that proposed algorithm performs well in comparison with prior state of the art algorithms. Proposed algorithm is independent of the density of fog and does not require user intervention. It can handle color as well as gray images. Proposed algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

Gaussian-Adaptive Bilateral Filter

Abstract: Recent studies have demonstrated that a bilateral filter can increase the quality of edge-preserving image smoothing significantly. Different strategies or mechanisms have been used to eliminate the brute-force computation in bilateral filters. However, blindly decreasing the processing time of the bilateral filter cannot further ameliorate the effectiveness of filter. In addition, even when the processing speed of the filter is increased, inherent problem occurred in the Gaussian range kernel when facing a noise filtering input and its effect on edge-preserving image smoothing operation are barely discussed. In this letter, we propose a novel Gaussian-adaptive bilateral filter (GABF) to resolve the aforementioned problem. The basic idea is to acquire a low-pass guidance for the range kernel by a Gaussian spatial kernel. Such low-pass guidance lead to a clean Gaussian range kernel for later bilateral composite. The results of experiments conducted on several test datasets indicate that the proposed GABF outperforms most existing bilateral-filter-based methods.