Image bilateral filtering method and device

Abstract

An embodiment of the invention provides an image bilateral filtering method and device. The method comprises the following steps: after obtaining pixel characteristic parameters of a pixel to be filtered in an image and neighborhood pixels thereof, carrying out table look-up operation based on the pixel characteristic parameters to obtain relevant parameters, and calculating pixel contribution sum and pixel weighting sum of all neighborhood pixels; and finally, through the pixel contribution sum, the pixel weighting sum and the pixel characteristic parameters of the pixel to be filtered, calculating a bilateral filtering result of the pixel to be filtered, and carrying out image processing on the pixel to be filtered according to the bilateral filtering result. The method is characterized by converting index calculation into table look-up operation, looking up a preset reciprocal table to obtain reciprocal of the pixel weighting sum and converting division operation into multiplication operation, thereby preventing a lot of floating-point precision parameter division and index operation, improving operation speed of the bilateral filtering result, reducing image processing waiting time and improving image processing efficiency and user experience.

Description

[0001] technology neighborhood [0002] The invention relates to the technical field of image processing, in particular to an image bilateral filtering method and device. Background technique [0003] In image processing, image filtering plays an important role. Image filtering can effectively suppress and smooth various noises, and maintain image edge information, thereby improving the quality of subsequent image processing. [0004] The bilateral filtering algorithm is a widely used filtering algorithm, which is often used in image filtering. It combines spatial weighting and pixel weighting, and can adapt to the local content of the image, and is widely used in image denoising, sharpening, dynamic range adjustment, etc. For a pixel (x, y) in the image, the bilateral filtering result B(x, y) is as follows: [0005] [0006] Wherein, x and y are the abscissa and ordinate of pixel I in the image respectively; Pixel (m, n) is the pixel near the pixel (x, y), that is, pixel ...

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Problems solved by technology

[0007] It can be seen from the above formula that the division and exponent calculation of a large number of floating-point precision parameters are involved in the formula. The speed of directly processing the division operation through the computer is relatively slow, and the speed of processing the exponent calculation is even slower, especially when the floating-point bit width is relatively small. Wide, it is difficult to perform high-density parallelization. Therefore, the current bilateral filtering algorithm is very slow, which makes the waiting time longer during image processing and reduces the user experience.

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