Affiliation:
1. Software Engineering Institute of Guangzhou, Guangzhou 511495, China
Abstract
With the rapid development of current technology, computer vision technology has also achieved better development, providing more possibilities for digital image generation. However, traditional digital image generation methods have high operational requirements for designers due to difficulties in collecting data sets and simulating environmental scenes, which results in poor quality, lack of diversity, and long generation speed of generated images, making it difficult to meet the current needs of image generation. In order to better solve these problems and promote the better development of digital image generation, this paper introduced a virtual interaction design under current popular blockchain technology in image generation, built a generative model through the virtual interaction design under the blockchain technology, optimized the image generation, and verified it in practice. The research results show that the digital image generation number of the method in this paper is 641. This indicates that method generates more images, has better diversity in digital image generation, and better image quality. Compared to other digital image generation methods, this method can better meet the needs of high-quality and diverse images, better adapt to the needs of different image generation, and is of great significance for promoting the better development of image generation.
Subject
Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)
Reference18 articles.
1. Text2human: Text-driven controllable human image generation;Jiang;ACM Trans. Graph. (TOG),2022
2. Cascaded Diffusion Models for High Fidelity Image Generation;Ho;J. Mach. Learn. Res.,2022
3. Defect Image Sample Generation with GAN for Improving Defect Recognition;Niu;IEEE Trans. Autom. Sci. Eng.,2020
4. Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification;Choi;J. Nucl. Med.,2018
5. Deep Virtual Reality Image Quality Assessment with Human Perception Guider for Omnidirectional Image. IEEE Trans;Kim;Circuits Syst. Video Technol.,2019
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