Researches On Multi-focus Image Fusion Based On Multi-scale Transformation And Deep Learning

With the advent of the information age,the interaction and application of infor-mation data has entered a stage of vigorous development.As one of the important representation forms of information,images are widely used in people's daily lives.Limited by the depth of field of the imaging system,there are often clear imaging of target scenes within the depth of field,but blurry imaging of scenes outside the range.Multi-focus fusion technology can complementally combine images of the same target scene with different focus areas,effectively solve the problem of clear imaging of the whole scene,and is of great significance in improving the utilization of image infor-mation.At present,this technology is increasingly widely used in many fields such as military,medical,civil life,aviation,and industrial vision.Multi-scale transformation is a method that simulates the analysis of images by the human visual perception system.It solves the problem of multi-focus image fusion by analyzing the characteristics of images at different scales.The multi-scale transform fusion method maps the source image to different frequency sub-bands through multi-scale decomposition,and then formulates fusion rules according to the characteristics of different sub-bands to guide the fusion of each component.This method improves the visual effect of the fused image very well,but it is often unable to accurately define the focus area of the source image,causing the fused image to have problems such as information redundancy.The deep learning model has powerful feature extraction capabilities and can better capture the general features of the image.The fusion method based on deep learning enables the fusion to achieve the focus area segmentation and makes the source image focus information transferred to the fusion image more abundant.This paper studies the characteristics of the above two types of image fusion methods,and its main work and innovation results are as follows:1.Convolution kernels of different scales can analyze image features from different levels of receptive fields.This article improves the convolutional neural network multi-focus image fusion method with twin structure based on this feature,and proposes a multi-scale convolutional neural network image fusion method.The network structure extends the width of the model through the multi-scale convolution kernel,makes the image feature extraction and analysis more comprehensive,and concatenates the shallow and deep features of the model,and makes full use of the learned features to improve the classification accuracy of the model.The algorithm uses this model to analyze the focus information of the source image to obtain a focus weight map corresponding to its focus area,and then process the weight map through binarization and morphological optimization to obtain a decision map,and finally the decision map guides multi-focusing image undergoes focusing information fusion.Experimental analysis shows that the performance of this method has been improved in terms of boundary segmentation of the focus area and feature extraction of small focus areas.2.In order to further improve the fusion image quality and enrich the image detail information,this paper proposes a convolutional neural network image fusion algorithm based on multi-scale details.This method uses rolling guided filtering to perform adaptive multi-scale decomposition,and decompose the source image into a base layer image and a different scale detail layer image.For multi-scale detail layer images,mapping through non-linear functions can enrich the image detail information,and then according to the decision graph obtained by the multi-scale convolutional neural network image fusion method,the fusion rules of the base layer and the multi-scale detail layer are respectively formulated to guide each layer image fusion,and finally the fusion image is obtained through image reconstruction of each layer.Experiments show that the method in this paper can effectively suppress image blocking and artifacts.While fully converging the focus information of the source image,it can make the fused image have rich details and excellent visual effects.