Generative Adversarial Networks for Visible to Infrared Video Conversion

Uddin, M.S.; Li, Jiang

doi:10.5772/intechopen.93866

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…In the early years, most of the image-to-image translation work involved two visible image domains, and very little research was on visible-to-IR image conversion. In recent years, researchers have worked on NIR (near-infrared)-tovisible image translation [14][15][16][17][18], MWIR to grayscale translation [19], and LWIR-to-RGB conversion [12,20] with some success in a supervised manner.…”

Section: Related Work 21 Image-to-image Translationmentioning

confidence: 99%

MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network

Uddin

Kwan

2023

Electronics

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Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and outperformed several competing state-of-the-art models. In addition, we customized the proposed model to convert game-engine-generated (a commercial software) images to MWIR images. The quantitative results showed that our proposed method could effectively generate MWIR images from game-engine-generated images, greatly benefiting MWIR data augmentation.

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Section: Related Work 21 Image-to-image Translationmentioning

confidence: 99%

MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network

Uddin

Kwan

2023

Electronics

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“…Wu, C. et al [11] proved that deep reinforcement learning can be successfully applied to a game with four directions of movement. The authors of [12] applied the pix2pix Generative Adversarial Network [13] and cycle-consistent GAN [14] models to convert visible videos to infrared videos. The authors of [15] proposed to focus on target areas using an Attention Generative Adversarial Network, which preserves the fidelity of target areas.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Stacked Siamese Generative Adversarial Nets: A Novel Way to Enlarge Image Dataset

2023

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Deep neural networks often need to be trained with a large number of samples in a dataset. When the training samples in a dataset are not enough, the performance of the model will degrade. The Generative Adversarial Network (GAN) is considered to be effective at generating samples, and thus, at expanding the datasets. Consequently, in this paper, we proposed a novel method, called the Stacked Siamese Generative Adversarial Network (SSGAN), for generating large-scale images with high quality. The SSGAN is made of a Color Mean Segmentation Encoder (CMS-Encoder) and several Siamese Generative Adversarial Networks (SGAN). The CMS-Encoder extracts features from images using a clustering-based method. Therefore, the CMS-Encoder does not need to be trained and its output has a high interpretability of human visuals. The proposed Siamese Generative Adversarial Network (SGAN) controls the category of generated samples while guaranteeing diversity by introducing a supervisor to the WGAN. The SSGAN progressively learns features in the feature pyramid. We compare the Fréchet Inception Distance (FID) of generated samples of the SSGAN with previous works on four datasets. The result shows that our method outperforms the previous works. In addition, we trained the SSGAN on the CelebA dataset, which consists of cropped images with a size of 128 × 128. The good visual effect further proves the outstanding performance of our method in generating large-scale images.

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“…Moreover, in [37], authors used conditional GAN to generate NIR spectral band from an RGB image where they used paired dataset for this conversion. In addition, cycle GAN [16,[38][39][40] was also used for visible-to-IR image translation.…”

Section: Image Conversion Between Visible and Ir Domainsmentioning

confidence: 99%

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

et al. 2021

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To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.

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Generative Adversarial Networks for Visible to Infrared Video Conversion

Cited by 5 publications

References 24 publications

MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network

MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network

Stacked Siamese Generative Adversarial Nets: A Novel Way to Enlarge Image Dataset

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

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