Ship Deck Segmentation Using an Attention Based Generative Adversarial Network

He, Joanna; Uddin, M.S.; Canan, M.; Sousa‐Poza, Andres; Kovacic, Samuel; Li, Jiang

doi:10.1109/uemcon54665.2022.9965690

Cited by 1 publication

(1 citation statement)

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“…We also noticed that CycleGAN cannot translate the details of the objects from visible to MWIR, and the shapes of objects were often distorted. To tackle this challenge, we proposed to use the perceptual loss, LPIPS [26], by following the idea in [30]. Typically, researchers use the LPIPS loss to evaluate the perceptual similarity between two images.…”

Section: Loss Functionmentioning

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: Loss Functionmentioning

confidence: 99%