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Proceedings ArticleDOI
Huiqian Du, Wenbo Mei 
30 Oct 2009
3 Citations
In addition, it can be used to resize images by any factor.
Thus display panels that do not have enough pixel numbers can be used to display the entire elemental images with a large number of pixels.
This paint is fast enough to resolve pressure changes ...
Proceedings ArticleDOI
07 Jun 2010
63 Citations
We demonstrate the effectiveness of our method in a real-time paint system implemented on the GPU that simulates pastel and oil paint.
Accordingly, in order to maintain the canvas as an effective support for the paint film, preventive conservatio...
The experimental results demonstrate that this novel approaches can decompose mixed pixels more accurately regardless of how much the endmember overlaps.
The conclusion from this study is that Image sizes below 320 pixels by 320 pixels should not be used.
This provides a path towards computer-assisted characterisation of paint materials on artwork.
Also, by predicting variations between integer-position pixels and sub-pixels, our network offers more expressive power.

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What is the difference between using AI and traditional methods for upscaling images?5 answersUsing AI for upscaling images offers several advantages over traditional methods. AI-based upscaling methods, such as the Object Detection AI proposed by Lau et al., focus on the objects within the images, resulting in improved image quality and reduced processing time. These methods utilize deep learning techniques and neural networks to generate high-resolution images from low-resolution ones, even when ground truth data is not available. In contrast, traditional methods rely on standard upscaling techniques, which may not prioritize the objects in the images and can be time-consuming. Additionally, AI-based upscaling methods, like the one presented by Jiang et al., allow for the estimation of permeability in large-scale core samples while preserving fine-scale pore structure variations. Overall, AI-based upscaling methods offer more efficient and accurate results compared to traditional methods.
How can AI be used to improve the quality of upscaled images?5 answersAI can be used to improve the quality of upscaled images by employing various techniques. One approach is to calculate a differential image during the rescaling process and use it to adjust the quality of the resulting upscaled image. Another method involves using AI-based image enhancement models that detect and remove unwanted objects or distracting elements from the image, resulting in a visually enhanced composition. Additionally, deep learning algorithms can be utilized to compensate for illumination drift and enhance low-illumination multi-scale images, effectively improving their quality. Furthermore, AI can aid in automatic image annotation by leveraging machine learning techniques and associated tags from social network websites, leading to improved performance in large-scale image management and retrieval. Finally, the use of AI-based super-resolution reconstruction models, such as the Multi-scale double Attention mechanism based on Residual Dense Generative Adversarial Network (MARDGAN), can enhance low-resolution images by extracting multi-scale features and improving perceptual capability.
Are we done with imagenet?3 answersRecent progress on the ImageNet classification benchmark may not represent meaningful generalization, as the community may have started to overfit to the idiosyncrasies of its labeling procedure. However, the errors in the original labels have been largely remedied by a new annotation procedure, reinforcing ImageNet as a powerful benchmark for future research in visual recognition. Deep neural network models are not easily interpretable, leading to a debate between blackbox and whitebox modeling in Artificial Intelligence (AI). The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has expanded the application space of AI. The dominance of AI by Big-Tech and the possible harms of new AI technologies have raised socio-technical issues such as transparency, fairness, and accountability. The University of Texas Medical School at Houston developed a successful teleradiology system for consultative radiologic services. IMAGENET, a local area network, has been developed for transferring large data packets in image and signal processing.
Which tool is used to change the size of an image or a selected part of an image?4 answersThe tool used to change the size of an image or a selected part of an image is an image processing device. This device can adjust the size of the input image and generate size-changed image data representing the new size of the image. It achieves this by determining a target image size based on the ratio of a predetermined standard character size to a character size in the original image data and the image size of the original image data. The image data creating unit then changes the size of the original image to create the size-changed image data based on the determined target size. Finally, the output control unit controls the output of the created size-changed image data to the output device, ensuring that the size-changed image is shown within the image formation area in the output image.
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