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Inspirational Adversarial Image Generation [PDF]
IEEE Transactions on Image Processing, 2021 The task of image generation started to receive some attention from artists and designers to inspire them in new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools.
Baptiste Roziere +5 more
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Generative convolution layer for image generation
Neural Networks, 2022 This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) performance. Unlike the standard convolution, GConv first selects useful kernels compatible with the given latent vector, and then linearly combines the selected kernels to make ...
Seung Park, Yong-Goo Shin
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Generative Image Inpainting for Retinal Images using Generative Adversarial Networks [PDF]
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed. However, a common drawback of these devices is a limited field of view.
Lucie Charlotte, Magister +1 more
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Generalized SMASH imaging [PDF]
Magnetic Resonance in Medicine, 2001 AbstractA generalized parallel imaging method has been developed that uses coil profiles to generate missing k‐space lines. The proposed method is an extension of SMASH, which uses linear combinations of coil sensitivity profiles to synthesize spatial harmonics. In the generalized SMASH approach described here, coil sensitivity profiles are represented
Mark, Bydder +2 more
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Generic Isolated Cell Image Generator [PDF]
Cytometry Part A, 2019 AbstractBuilding automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of the biggest challenges of such systems, especially those using state‐of‐the‐art deep learning techniques, is that they usually require a large amount of training data to be accurate.
Scalbert, Marin +2 more
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Generative imaging and image processing via generative encoder
Inverse Problems & Imaging, 2022 <p style='text-indent:20px;'>This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and super-resolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of ...
Ong, Yong Zheng, Yang, Haizhao
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An Overview of Image Generation of Industrial Surface Defects
Sensors, 2023 Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-
Xiaopin Zhong +5 more
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Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms
Mathematics, 2023 In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network.
Ying Li, Ye Tang
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A GENETIC APROACH FOR AUTOMATED IMAGE GENERATION: GRAYSCALE IMAGE GENERATION
Journal of Engineering, 2005 Non photorealistic rendering is a new research field in the areas of computer graphics. The goal is to give a more natural feel to computer generated images, by simulating various artistic techniques and to give the sense of an image without reproducing
Bara'a Ali Attea, Aminna Dahim Aboud
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Applied Sciences, 2023 Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors.
Haojun Qin, Lei Zhang, Quan Guo
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