Gray level image median filtering optimizing method based on FPGA and system thereof

Abstract

The invention provides a gray level image median filtering optimizing method based on an FPGA (Field Programmable Gate Array) and a system thereof, wherein the method comprises the following steps of Step 1, setting top layer input and output ports, and connecting all function control modules at a bottom layer through a connecting line; Step 2, sequentially inputting image data from the input port, and storing the image data in a filter window; sliding the whole image through the filter window along with the continuous input of the image data; and further obtaining and processing all pixels in the image; Step 3, obtaining a first center pixel point after a starting signal; calculating the row and line information of the center pixel point; and determining the row and line position of the data obtained from a window template in the complete image, and determining whether the window reaches the edge or not or whether the transmission is completed or not so as to perform the edge control; and Step 4, optimizing the image by using a median filtering optimizing algorithm.

Description

Technical field [0001] The invention relates to the field of gray-scale image filtering, in particular to an FPGA-based gray-scale image median filtering optimization algorithm and its system. Background technique [0002] In recent years, with the increasing application of computer vision technology and digital image processing in various fields, in some real-time inspection occasions, the requirements for real-time and parallelism of image data processing are getting higher and higher. Generally, in the process of image signal formation, storage and transmission, due to inherent defects of the imaging system, storage device, transmission medium and working environment, or random interference caused by transient sensor abnormality and channel noise, the image quality deteriorates and seriously affects The effect of subsequent processing. In order to eliminate noise and improve image quality, preprocessing operations such as filtering and smoothing must be performed on the image...

AI Technical Summary

Problems solved by technology

Usually, in the process of image signal formation, storage and transmission, due to the inherent defects of imaging system, storage device, transmission medium and working environment, or the random interference caused by sensor transient aberration and channel noise, the image quality is deteriorated, seriously affecting The effect of subsequent processing

In order to eliminate noise and improve image quality, preprocessing operations such as filtering and smoothing must be performed on the image, but traditional filtering algorithms usually cannot meet the real-time requirements in the case of a large amount of data sorting

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