Study On Microscopic Image Preprocessing Based On Visual Features

With the continuous development of modern science and technology,microscopic equipment with its ultra-high resolution has played a huge role in human understanding of the microscopic world.However,in the process of image acquisition and recording,due to the influence of the dark current of the device,the physical properties of the sample,and the subjective judgment of the operator,the image will have varying degrees of noise and contrast imbalance.These distortion conditions lead to the degradation of the quality of the microscopic image,and the direct observation and study of the degraded image will cause errors in the analysis of the target.Therefore,it is very important to preprocess the microscopic image to improve the image quality.This thesis takes the two important types of equipment in microscopy instruments:Scanning electron microscope?SEM?and X-ray microtomography?XRM?images as the research object.This thesis analyzes the main distortion factors that affect the quality of the two types of images,studies the image denoising and enhancement preprocessing methods,improves the details of the image,and provides high-quality images for subsequent target segmentation and recognition.The main research contents of this thesis include the following two points:First,to solve the problem of imbalance in image contrast caused by low mineral content in coal and small density difference in XRM coal image,a multiscale high dynamic range image tone mapping method based on human visual system is proposed.In order to enrich the feature details of the target image,this method uses guided filter to decompose the image at multiple scales to extract the structural features in each layer;for the imbalance of the image contrast and the poor visual effect,this paper constructs the original histogram based on the perception effect of the human visual system,and use the unit step to reduce the pixel count to construct a new histogram.Finally,a weighted combination of the old and new histograms is used to construct a mapping histogram;the features extracted by multiscale decomposition are added to the improved mapping histogram to enrich the image features while achieving contrast enhancement.Experimental results show that this method not only effectively improves the visual effect of the image,but also obtains high affirmation in objective image quality evaluation.Second,in view of the problem that the image is susceptible to noise caused by the scanning time and the device circuit during the SEM imaging process,an SEM image denoising algorithm based on an improved U-net network is proposed.Considering the problem that the output value of rectified linear unit?ReLU?in the original U-net network is 0 after entering the negative range,which leads to the neuron silence,the leaky rectified linear unit?LReLU?is introduced to modify,so that the neuron with negative threshold can be trained and updated during the network training,and the network fitting ability can be improved at the same time;The original u-net network training needs a large number of pairs of noisy images and clear images,but the SEM clear image acquisition is difficult,so this method introduces L₂ loss function to maintain the original network under the condition of using only noise images for network training denoising effect.This loss function can replace the noiseless distribution with the noise distribution by the same conditional expectation without changing the network learning content.It is proved by experiments that the improved U-net network uses only noisy images for training on the SEM image set,which has a good denoising effect and reduces the difficulty of making the training data set.The thesis has 23 figures,4 tables and 98 references.