Binarization image and gray level image efficient denoising method based on pixel continuity judgment

Abstract

The invention discloses a binarization image and gray level image efficient denoising method based on pixel continuity judgment, and belongs to the field of image processing. According to the characteristics that noise point distribution is relatively disperse and separate and signal points are relative continuous, whether pixels with the same luminance are noise or not is directly judged through the continuity of the pixels with the same luminance in binarization images, pixels of the same kind in gray level images are defined in the gray level images firstly, and whether the pixels of the same kind are noise or not is judged based on the continuity of the pixels of the same kind; denoising effects are achieved on noise points in the binarization images by directly changing the luminance values of the noise points into opposite luminance values of the noise points, and filter and denoising effects are achieved by replacing the luminance values of the noise pixel points in the gray level images with the average value of the luminance values of heterogeneous pixel points on the periphery of the noise pixel points in the gray level images. The algorithm for judging and denoising is simple, executing efficiency is high, denoising effects are good, and losing of image information can be avoided.

Description

Technical field: [0001] The invention relates to an efficient denoising method for binarized images and grayscale images based on pixel continuity judgment, and belongs to a method for removing noise in the field of image processing. Background technique: [0002] The application field of digital images is very wide. For image processing technology, the most important step is to obtain relevant image objects to be processed and analyzed. The conditions for image acquisition are sometimes limited, which leads to the acquired image signal. Interference or introduction of various types of digital image noise will not be conducive to the subsequent processing and analysis of the image. [0003] There are many factors that cause image degradation, and different factors lead to different types of noise. Due to the existence of noise, subsequent image processing tasks (such as image edge detection, image segmentation, image target recognition, etc.) have been expanded. Adverse effects. ...

AI Technical Summary

Problems solved by technology

Neural network tools can also be used to remove digital image noise. A digital image noise detector can be built through the neural network model to detect image pixels to see if they are noise points or signal points, and then pass various Different methods only filter the noise points. This type of method needs to train the sample data set to obtain the neural network model. Here, the acquisition of the sample data set is crucial to the accuracy of the neural network model judgment. It can remove noise very well and obtain a better denoising effect, but this method is more complicated, the amount of calculation is relatively large, and a large amount of training data and training images are required.

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