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Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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2018 |
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25th International Conference on Image Processing |
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2237 - 2241 |
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Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization |
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This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
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Athens; Greece; October 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018b |
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3195 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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Conference Article |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
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1250 - 12507 |
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Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
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This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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Salt Lake City; USA; June 2018 |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ SSV2018d |
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3197 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
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2019 |
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IEEE International Conference on Computer Vision and Pattern Recognition-Workshops |
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This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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Long beach; California; USA; June 2019 |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ SSV2019 |
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3272 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation |
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Conference Article |
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2021 |
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28th IEEE International Conference on Image Processing |
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19-22 |
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This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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Anchorage-Alaska; USA; September 2021 |
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MSIAU; 600.130; 600.122; 601.349 |
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Admin @ si @ SSV2021b |
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3579 |
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