Recent Advances in Image Restoration With Applications to Real World Problems 2020
DOI: 10.5772/intechopen.93866
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Generative Adversarial Networks for Visible to Infrared Video Conversion

Abstract: Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases … Show more

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Cited by 5 publications
(3 citation statements)
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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%
“…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%
“…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%
“…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%