Detail enhancement and segmentation extraction of cladding image based on super resolution

Aiming at the images of relevant monitored objects in the process of laser cladding, a super resolution algorithm technology was proposed to optimize and enhance the key details of the images, and the enhanced image content was segmented, extracted and counted. First, construct a training of a sub-resolution convolutional neural network (SRCNN) model; the original low resolution is predicted by the weight after training, the image quality evaluation results: peak signal-to-noise ratio (PSNR) is 30.198212, structural similarity (SSIM) is 0.969966; the most based on the maximum entropy dual threshold split algorithm combined with image processing, extracting and statistics on the powder object in the segmentation result image, the number of effective powders and proportion of the original wandering map and the predicted output delay image is [112, 33.6%] and [240, 40.6%]. The research results show that the cladding image output from the original image after the super resolution model has been significantly improved in terms of clarity and quality as well as the optimization and enhancement of the details of the monitored object.