Abstract
Computer vision is currently playing an increasingly important role in automatically identifying the character of the image processing technology as research hotbed in the field of smart computing, OCR, face recognition, fingerprinting, biometric recognition, and so forth. Content-based image recovery, video recovery, multimedia collection, watermarking, games, film stunts, virtual reality, e-commerce, and other apps are available all round. The color pictures of parts taken by industrial cameras depend on computer performance and the intricate environment, and in particular, on the whole resolution image display, a lot of CPU resources are needed. Some details cannot be shown completely at the same time. If the image is not sufficiently clearly visible, methods for image processing like improvement, noise reduction, and interpolation must be used to improve color photo clarity. This article, based on the OpenCV platform, uses frequency domain filters, median filters, Fourier transform, and other image improvement technologies to remove image noise in order to enhance the quality of local photos from industrial cameras’ components. Finally, clear and available image information is obtained in different experimental methods, which check the application of image enhancement technology to image rebuilding. Finally, the performance of the proposed method in terms of CPBD value, definition Q value, and operation time is compared, which shows that the proposed method has obvious advantages in the above performance.
Highlights
Computer vision has been implemented in image recognition, cut, important information capture, and other areas as a consequence of the rapid development of deep learning and artificial intelligence technology
The second part of this paper summarizes the research status of image processing, the third part proposes Fourier transform and frequency domain filtering processing methods, the fourth part proposes to enhance the blurring of industrial images, and the fifth part carries out experimental verification and performance evaluation on the proposed methods
Based on the principle of frequency domain filtering for image enhancement, we randomly selected 100 true color images of components from the blurred images captured by industrial cameras and carried out experiments on the OpenCV platform to obtain the images after low-pass filtering
Summary
Computer vision has been implemented in image recognition, cut, important information capture, and other areas as a consequence of the rapid development of deep learning and artificial intelligence technology. In 2014, the world-famous internet company recognized a cat from many photos through powerful computer resources which made great breakthroughs in computer technology and in the field of machine learning. With the promotion of algorithm progress, mainstream AI companies have reduced the data volume of identifying target objects from 10 million pictures to 80000, and data annotation and image processing methods play an indispensable role in the application of computer vision. The filter algorithm is used to remove the noise in the image, and the image enhancement technology is introduced to make the image have a higher resolution and clearer image. If the image is not clear enough, image processing techniques such as enhancement, noise reduction, and interpolation can be used to enhance the image display resolution
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