A Dataset of Flash and Ambient Illumination Pairs from the Crowd

Aksoy, Yağız; Kim, Chang-Il; Kellnhofer, Petr; Paris, Sylvain; Elgharib, Mohamed; Pollefeys, Marc; Matusik, Wojciech

doi:10.1007/978-3-030-01240-3_39

Cited by 38 publications

(33 citation statements)

References 36 publications

(51 reference statements)

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“…Finally, we present the controlled experiments that we perform to determine how the components of our architecture affect the overall performance. Introduced by (Aksoy et al, 2018), the FAID(Flash and Ambient Illumination Dataset) is a collection of pairs of flash and ambient images, which present 6 categories: People, Shelves, Plants, Toys, Rooms, and Objects. As a result, we have 2775 pairs of flash and ambient images.…”

Section: Methodsmentioning

confidence: 99%

“…In this image, the illumination is more uniform, natural, and the image has not sideways shadows. Images extracted from FAID (Aksoy et al, 2018). ows. Second, the adversarial loss, which represents the objective function of GANs (Goodfellow et al, 2014), forces to model high-frequency details on the output image, and perform a more natural illumination.…”

Section: Introductionmentioning

confidence: 99%

“…Ambient images from FAID(Aksoy et al, 2018) with low illumination, reflections, and shadows from external objects.…”

mentioning

confidence: 99%

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Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

Chávez

Mora

Cayllahua-Cahuina

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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To cope with the challenges that low light conditions produce in images, photographers tend to use the light provided by the camera flash to get better illumination. Nevertheless, harsh shadows and non-uniform illumination can arise from using a camera flash, especially in low light conditions. Previous studies have focused on normalizing the lighting on flash images; however, to the best of our knowledge, no prior studies have examined the sideways shadows removal, reconstruction of overexposed areas, and the generation of synthetic ambient shadows or natural tone of scene objects. To provide more natural illumination on flash images and ensure high-frequency details, we propose a generative adversarial network in a guided conditional mode. We show that this approach not only generates natural illumination but also attenuates harsh shadows, simultaneously generating synthetic ambient shadows. Our approach achieves promising results on a custom FAID dataset, outperforming our baseline studies. We also analyze the components of our proposal and how they affect the overall performance and discuss the opportunities for future work.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

Chávez

Mora

Cayllahua-Cahuina

2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…We use the new flash/non-flash image dataset provided by [66] for both training and testing. We randomly select 400 flash/non-flash image pairs from the dataset [66] for training. For testing, to make the results more convincing, we select 12 images from three different categories in [66], i.e., the toy, plant and object.…”

Section: Flash Guided Non-flash Image Denoisingmentioning

confidence: 99%

“…We randomly select 400 flash/non-flash image pairs from the dataset [66] for training. For testing, to make the results more convincing, we select 12 images from three different categories in [66], i.e., the toy, plant and object. Note that the testing images are different from the training images.…”

Section: Flash Guided Non-flash Image Denoisingmentioning

confidence: 99%

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deng

Dragotti

2021

IEEE Trans. Pattern Anal. Mach. Intell.

140

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In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., Common and Unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion. Index Terms-Multi-modal image restoration, image fusion, multi-modal convolutional sparse coding ! • The authors are with the

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An Efficient Image Transmission Pipeline for Multimedia Services

Wang¹

2021

Lecture Notes in Computer Science

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A Dataset of Flash and Ambient Illumination Pairs from the Crowd

Cited by 38 publications

References 36 publications

Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

Ambient Lighting Generation for Flash Images with Guided Conditional Adversarial Networks

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

An Efficient Image Transmission Pipeline for Multimedia Services

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