| Autor: |
Dennis Estrada, Fraser Dalgleish, Caitlin Smith, Madison Young, Bing Ouyang, Joseph Desjardins, Susanne Lee, Casey Den Ouden |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Big Data II: Learning, Analytics, and Applications. |
| DOI: |
10.1117/12.2560792 |
| Popis: |
While machine learning-based image restoration techniques have been the focus in recent years, these algorithms are not adequate to address the effects of a degraded visual environment. An algorithm that successfully mitigates these issues is proposed. The algorithm is built upon the state-of-the-art DeblurGAN algorithm but overcomes several of its deficiencies. The key contributions of the proposed techniques include: 1)Development of an effective framework to generate training datasets typical of a degraded visual environment; 2) Adopting a correntropy based loss function to integrate with the original VGG16 based perceptual loss function and an L1 loss function; 3) Conducting substantial experiments against images from the artificial training datasets and demonstrate the effectiveness of the proposed algorithm. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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