Broad-band co-phase detection based on denoising convolutional neural network
The co-phase error detection of segmented mirrors is currently a critical focus of scientific re-search.Co-phase detection technology based on a broad-band light source solves the problem of long meas-urement times caused by the Shackle-Hartmann method's low target flow rates,thereby improving the accur-acy and range of piston error detection.However,in the application of the current broad-band algorithm,the complex environment and the presence of disturbing factors such as camera perturbations cause the acquired circular aperture diffraction images to contain a certain amount of noise,which leads to a correlation coeffi-cient value below the set threshold,reduces the accuracy of the method,and even makes it ineffective.To solve the problem,we propose a method by integrating an algorithm based on Denoising Convolutional Neural Network(DnCNN)into the broad-band algorithm in order to control the noise interference and retain the phase information of the far-field image.First,the circular hole diffraction image obtained by using MATLAB is used as the training data for DnCNN.After the training,the images with different noise levels are imported into the trained noise reduction model to obtain the denoised image as well as the peak signal-to-noise ratios of the circular hole diffraction images before and after denoising.The structural similarity between the two images and the clear and noiseless image are also obtained.The results indicate that the av-erage structural similarity between the denoised image and the ideal clear image has significantly improved compared to the image before processing,and this achieves an ideal denoising effect,which effectively in-creases the ability of broad-band algorithms to cope with the effects of high noise conditions.This study has strong theoretical significance and application value for exploring the broad-band light source algorithm for applications in practical co-phase detection environments.
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