From Coarse to Fine (FC2F): A New Scheme of Colorizing Thermal Infrared Images

Cheng, Feiyan; Shi, Junsheng; Yun, Lijun; Cao, Xuebing; Zhang, Jun

doi:10.1109/access.2020.3000481

Cited by 6 publications

(1 citation statement)

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“…[ 246 ] Instantly, contextual improvement is essential for environmental perception in night vision applications, particularly in low illumination constraints of the dark night. [ 247 ] To address this, colorization [ 248 ] and data fusion [ 249 ] of the target objects are good translational tools to visualized thermal depiction into colorized visible images and reflects overall features of the targeted objects. Liu et al., [ 247 ] suggested two steps for unsupervised smart sensing image translation neural network for infrared to visible (IR2VI) translation of night time thermal imaging includes, first translating thermal infrared images to gray‐scale visible images (GVI), denoted as IR‐GVI and then translation to color visible images denoted as (CVI).…”

Section: Applicationsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

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Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro‐scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

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Section: Applicationsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

Small

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Exploring efficient and effective generative adversarial network for thermal infrared image colorization

Chen¹,

Zhan²,

Jiang³

et al. 2023

Complex Intell. Syst.

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Thermal infrared image colorization is very difficult, and colorized images suffer from poor texture detail recovery and low color matching. To solve the above problems, this paper proposes an Efficient and Effective Generative Adversarial Network (E2GAN). This paper proposes multi-level dense module, feature fusion module, and color-aware attention module in the improved generator. Adding multi-level dense module can enhance the feature extraction capability and the improve detail recovery capability Using the feature fusion module in the middle of the encoder–decoder reduces the information loss caused by encoder down-sampling and improves the prediction of fine color of the image. Using the color-aware attention module during up-sampling allows for capturing more semantic details, focusing on more key objects, and generating high-quality colorized images. And the proposed discriminator is the PatchGAN with color-aware attention module, which enhances its ability to discriminate between true and false colorized images. Meanwhile, this paper proposes a novel composite loss function that can improve the quality of colorized images, generate fine local details, and recover semantic and texture information. Extensive experiments demonstrate that the proposed E2GAN has significantly improved SSIM, PSNR, LPIPS, and NIQE on the KAIST dataset and the FLIR dataset compared to existing methods.

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