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- 01HS0Q2B7DQFJGRW8K7FZ140CZ classification C1.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ date "2023".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ language "eng".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ type conference.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ hasPart 01HS0Q4Q6S9MP5HK922WN2ETQK.pdf.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ hasPart 01J91CV9QTV734FA6Z92MMHYKZ.pdf.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ subject "Technology and Engineering".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ doi "10.58286/28108".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ issn "2941-4989".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ issue "1".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ presentedAt urn:uuid:ae043e75-c553-4fb7-907b-187021cc4381.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ volume "1".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ abstract "Infrared thermography is a valuable non-destructive tool for inspection of materials. It measures the surface temperature evolution, from which hidden defects may be detected. Yet, thermal cameras typically have a low native spatial resolution resulting in a blurry and low-quality thermal image sequence. In this study, an adversarial deep learning framework, called Dual-IRT-GAN, is proposed for performing super-resolution tasks. The proposed Dual-IRT-GAN has the objective of improving local texture details, as well as highlighting defective regions. Technically speaking, the proposed model consists of two modules SEGnet and SRnet that implement defect detection and super resolution, respectively. By leveraging the defect information from SEGnet, SRnet is capable of generating plausible high-resolution thermal images with an enhanced focus on defect regions. The generated high-resolution images are then delivered to the discriminator for adversarial training using GAN's framework. The proposed Dual-IRT-GAN model, which is trained on an exclusive virtual dataset, is demonstrated on experimental thermographic data obtained from fiber reinforced polymers having a variety of defect types, sizes, and depths. The obtained results show its high performance in maintaining background color consistency and removing undesired noise, and in highlighting defect zones in high-resolution.".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ author 199EDBAE-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ author 5A33E6E2-CD04-11E9-8B81-BA085707D3EF.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ dateCreated "2024-03-15T09:28:39Z".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ dateModified "2024-11-28T00:13:57Z".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ name "Defect-aware super-resolution thermography by adversarial learning".
- 01HS0Q2B7DQFJGRW8K7FZ140CZ pagination urn:uuid:d593983e-6186-4e0b-b53c-80a5c1497541.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ sameAs LU-01HS0Q2B7DQFJGRW8K7FZ140CZ.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ sourceOrganization urn:uuid:be9928ed-b5fb-4a2c-8dfa-90b0df5931a7.
- 01HS0Q2B7DQFJGRW8K7FZ140CZ type C1.