Deep learning-based infrared image deblurring

Ninh, Huong; Thai, Chien; Hai, Tran Tien

doi:10.1117/12.2636946

Cited by 2 publications

(1 citation statement)

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“…However, the commonly used industrial camera lost a lot of useful information when collecting images in extreme environmental conditions, such as darkness and haze. Infrared thermal image obtained using thermal infrared detection technology made the target have certain degree of recognition by reflecting the thermal radiation information emitted by the target, effectively compensating the information loss of visible light image in harsh imaging environment [12][13][14][15][16][17][18][19][20][21][22][23]. Therefore, the research on the target area extraction algorithm of infrared thermal image is very important for improving the detection robustness and accuracy of interested targets in harsh environment.…”

Section: Introductionmentioning

confidence: 99%

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

Yang¹

2023

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Infrared thermal image makes the target have certain degree of recognition by reflecting the thermal radiation information emitted by the target, which effectively compensates the information loss of visible light image in harsh imaging environment. Contour extraction effect of target area using traditional Canny algorithm is not good, because the contour gradient change of infrared thermal image target area is not obvious. At the same time, the threshold of most of algorithms needs to be set manually, which is greatly affected by subjective factors, and the image processing efficiency is low. Therefore, this paper studied the target area extraction algorithm of infrared thermal image by combining target detection with matching correction. First, the paper introduced the feature matching algorithm based on grid motion statistics, and converted smoothness constraint of motion into statistics, thus replacing the number of extended feature points with the acquisition of features with better performance and filtering false image matching based on the number of other matching points in the neighborhood of statistical matching points. Second, based on the feature matching results obtained in the previous section, this paper proposed a extraction method of infrared thermal image target area based on thermal feature descriptors, which combined the extracted thermal features with the semantic attributes of each area in the infrared thermal image, thus distinguishing the subtle differences between the infrared thermal image sub-categories. Finally, experimental results verified the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

Yang¹

2023

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4-K-resolution minimalist optical system design based on deep learning

Meng,

Zhou,

Bai

2024

Appl. Opt.

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In order to simplify optical systems, we propose a high-resolution minimalist optical design method based on deep learning. Unlike most imaging system design work, we combine optical design more closely with image processing algorithms. For optical design, we separately study the impact of different aberrations on computational imaging and then innovatively propose an aberration metric and a spatially micro-variant design method that better meet the needs of image recognition. For image processing, we construct a dataset based on the point spread function (PSF) imaging simulation method. In addition, we use a non-blind deblurring computational imaging method to repair spatially variant aberrations. Finally, we achieve clear imaging at 4 K (5184×3888) using only two spherical lenses and achieve image quality similar to that of complex lenses on the market.

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Deep learning-based infrared image deblurring

Cited by 2 publications

References 10 publications

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

4-K-resolution minimalist optical system design based on deep learning

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