نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده مهندسی شیمی و نفت دانشگاه صنعتی شریف
2 دانشکده شیمی و نفت، دانشگاه صنعتی شریف، تهران، ایران
3 دانشکده مهندسی شیمی و نفت، دانشگاه صنعتی شریف، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Reconstructing high-resolution images from poor-quality images is one of the basic challenges in image processing. In modeling porous media using images obtained from computed microtomography (Micro-CT), high-quality images of these media are usually unavailable for different reasons, including the cost and computational complexity of high-quality imaging. With the advancement in the development of multiscale networks, it is possible to use more image details in these networks, and there is a further need for high-quality images. Today, adversarial generative networks are used as a practical tool in increasing image quality. These networks are trained using pairs of high-quality and low-quality images and then, they produce high-quality images by taking low-quality images as input. The final images used in modeling the porous media are binary images, but using binary images as input in the image quality enhancement process may result in detail loss. For this purpose, this study investigates the effect of using grayscale or binary images as input in the model training process. Grayscale images of a Brea sandstone sample are taken as the main input and binary images are generated using grayscale images and the realization of the real porosity of the rock sample. In this research, a new model called RealESRGAN is used to enhance the quality of the images.
کلیدواژهها [English]
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