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AOGAN:A generative adversarial network for screen space ambient occlusion

Computational Visual Media 2022年第3期8卷 483-494页

作者： Lei Ren Ying Song Zhejiang Sci-Tech University Hangzhou 310018China 2011 Collaborative Innovation Center for Garment Personal Customization of Zhejiang Province China Key Lab of Silk and Culture Heritage and Products Design Digital Technology Ministry of Culture and TourismChina

Ambient occlusion(AO)is a widely-used real-time rendering technique which estimates light intensity on visible scene ***,a number of learning-based AO approaches have been proposed,which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and ***,most such methods have high error for complex scenes or tend to ignore *** propose an end-to-end generative adversarial network for the production of realistic AO,and explore the importance of perceptual loss in the generative model to AO *** attention mechanism is also described to improve the accuracy of details,whose effectiveness is demonstrated on a wide variety of scenes.

关键词： ambient occlusion(AO) attention mechanism generative adversarial network(GAN) perceptual loss

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维普期刊数据库

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Unpaired image to image transformation via informative coupled generative adversarial networks

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Frontiers of Computer Science 2021年第4期15卷 83-92页

作者： Hongwei GE Yuxuan HAN Wenjing KANG Liang SUN College of Computer Science and Technology Dalian University of TechnologyDalian 116024China

We consider image transformation problems,and the objective is to translate images from a source domain to a target *** problem is challenging since it is difficult to preserve the key properties of the source images,and to make the details of target being as distinguishable as *** solve this problem,we propose an informative coupled generative adversarial networks(ICoGAN).For each domain,an adversarial generator-and-discriminator network is ***,we make an approximately-shared latent space assumption by a mutual information mechanism,which enables the algorithm to learn representations of both domains in unsupervised setting,and to transform the key properties of images from source to ***,to further enhance the performance,a weightsharing constraint between two subnetworks,and different level perceptual losses extracted from the intermediate layers of the networks are *** quantitative and visual results presented on the tasks of edge to photo transformation,face attribute transfer,and image inpainting,we demonstrate the ICo-GAN’s effectiveness,as compared with other state-of-the-art algorithms.

关键词： generative adversarial networks image transformation mutual information perceptual loss

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维普期刊数据库

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A detail preserving neural network model for Monte Carlo denoising

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Computational Visual Media 2020年第2期6卷 157-168页

作者： Weiheng Lin Beibei Wang Lu Wang Nicolas Holzschuch School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China School of Computer Science and Technology Shandong UniversityJinan250100China Univ.Grenoble-Alpes InriaCNRSGrenoble INPLJK38000GrenobleFrance

Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel,resulting in long rendering time. To reduce the cost,one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel(as little as128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning:they use convolutional neural networks to learn the relationship between noisy images and reference images,using auxiliary features such as position and normal together with image color as inputs. The network predicts kernels which are then applied to the noisy input. These methods show powerful denoising ability,but tend to lose geometric or lighting details and to blur sharp features during *** this paper, we solve this issue by proposing a novel network structure, a new input feature—light transport covariance from path space—and an improved loss function. Our network separates feature buffers from the color buffer to enhance detail effects. The features are extracted separately and then integrated into a shallow kernel predictor. Our loss function considers perceptual loss, which also improves detail *** addition, we use a light transport covariance feature in path space as one of the features, which helps to preserve illumination details. Our method denoises Monte Carlo path traced images while preserving details much better than previous methods.

关键词： deep learning light transport covariance perceptual loss Monte Carlo denoising

来源：

维普期刊数据库

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MRI-Trans-GAN: 3D MRI Cross-Modality Translation

MRI-Trans-GAN: 3D MRI Cross-Modality Translation

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第40届中国控制会议

作者： Pu Zhao Hong Pan Siyu Xia School of Automation Southeast University

Multi-modality magnetic resonance imaging（MRI） data prove more effective than a single modality in diagnosis, yet data collection for paired MRI images is a challenge. It is usually time consuming and may raise privacy concern. In this study,we explore cross-modality translation between MRI T1-weighted and T2-weighted images with 3D dual conditional generative adversarial networks（CGANs）, and aim to synthesize missing modality of MRI images to mitigate the issue of insufficient data. To that end, a novel 3D-method, MRI translation generative adversarial network（MRI-Trans-GAN） is established. First,different from existing 3D big-patch methods, MRI-Trans-GAN is based on modified 3D patches with larger size in sagittal and coronal（frontal） axes and smaller size in vertical axis, i.e., 128 × 128 × 3 voxels. In addition, the tensor shape in vertical axis is fixed in forward process, and as a result, our model uses less memory than 3D big-patch methods. Moreover, it considers voxels relationship among adjacent slice other 2D-methods ignore. Second, we investigate loss functions and leverage wasserstein distance with gradient penalty and spectral normalization over traditional adversarial losses for better image quality and ***, extensive experiments demonstrate the superiority of the proposed model over the state-of-the-art. It shows that our method renders better quality in terms of peak signal to noise ratio（PSNR） and structural similarity（SSIM）.

关键词： Cross-modality translation WGAN-GP spectral normalization perceptual loss

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