Image fuzzy processing method and electronic device

Abstract

The invention discloses an image fuzzy processing method and an electronic device, and relates to the technical field of image processing. The method herein includes the following steps: acquiring the pixel value of a current pixel point in an image in preset order; for the acquired each current pixel point, based on a plurality of pixel points which are adjacent in a left and right manner to the current pixel point, computing the average value of the current pixel point and the pixel values of the plurality of pixel points which are adjacent in a left and right manner; and replacing the original pixel value of the current pixel point with the average value. According to the invention, by computing the average value of the current pixel point and the pixel values of the plurality of pixel points which are adjacent in a left and right manner for each current pixel point, replaces the original pixel value of the current pixel point with the average value, such that all pixel points of the image are replaced with the corresponding average value and fuzzy processing of the image is realized. Since the process simply relates to the computing of average value, the method herein reduces computing amount and computing complexity, increases computing efficiency, avoids over-usage of the processing resources of the electronic device, and improves user experience.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image blurring processing method and electronic equipment. Background technique [0002] Now, in the field of image processing, Gaussian blur technology is widely used to process images. Since Gaussian blur can reduce image noise and reduce the level of detail, it can enrich the aesthetics of the human-computer interaction interface of mobile devices, so whether it is a single picture, It is also of great significance to perform real-time and efficient blurring processing effects on video frame images during video playback or instant video. [0003] The traditional Gaussian blur is to use the normal distribution to calculate the blur template, and use the template to perform convolution operation with the original image to achieve the purpose of blurring the image. During the calculation process, calculating the Gaussian blur value of a certain point requires multiple pixels ar...

AI Technical Summary

Problems solved by technology

[0003] The traditional Gaussian blur is to use the normal distribution to calculate the blur template, and use the template to perform convolution operation with the original image to achieve the purpose of blurring the image. During the calculation process, calculating the Gaussian blur value of a certain point requires multiple pixels around it. Point, and to calculate the weight matrix of each pixel, calculate the Gaussian blur value of a certain pixel according to the weight matrix, and perform this process for each pixel in the image, the calculation amount is large, and the calculation process It is more complicated; at the same time, in the traditional Gaussian blur calculation, the read pixel values ​​are read one by one, which further increases the time consumption of Gaussian blur processing on the image, and Gaussian blur uses floating-point operations, which further increases the processing time. Time, when running a video playback process or a real-time video program and performing Gaussian blur, it will take up a lot of processing resources, cannot be processed quickly, and the user experience is not good

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