A study on selective dehazing system based on haze detection network
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초록
Image dehazing, which aims to recover a clear image solely from a hazy or foggy image, is a particularly challenging task. Many studies have recently been conducted to improve the performance of image dehazing using deep neural networks. However, existing approaches do not consider changes in haze density, and thus even if a clear image is input, distortions such as sharpening may occur. In addition, because the number of datasets available in deep learning, whose contents are image pairs of hazy and corresponding haze-free (ground truth) indoor images, is quite limited, the haze removal performance may be reduced. To solve this problem, in this paper, a selective dehazing system is proposed that combines a haze detection network and a dehazing network. The proposed haze detection network is designed using a CNN structure to determine the haze density of the input image, and the use of the dehazing network is determined. The proposed dehazing network uses the U-Net model to efficiently learn only a limited number of datasets. To evaluate the performance of the proposed network, only 45 O-Hazes were used. The result of haze detection shows that the proba-bility of detecting a haze image is more than 99% and the probability of detecting a haze-free image is 97.9%. Dehazing evaluation results improved the PSNR and SSIM by more than 10% compared to existing networks.
Abstract
Image dehazing, which aims to recover a clear image solely from a hazy or foggy image, is a particularly challenging task. Many studies have recently been conducted to improve the performance of image dehazing using deep neural networks. However, existing approaches do not consider changes in haze density, and thus even if a clear image is input, distortions such as sharpening may occur. In addition, because the number of datasets available in deep learning, whose contents are image pairs of hazy and corresponding haze-free (ground truth) indoor images, is quite limited, the haze removal performance may be reduced. To solve this problem, in this paper, a selective dehazing system is proposed that combines a haze detection network and a dehazing network. The proposed haze detection network is designed using a CNN structure to determine the haze density of the input image, and the use of the dehazing network is determined. The proposed dehazing network uses the U-Net model to efficiently learn only a limited number of datasets. To evaluate the performance of the proposed network, only 45 O-Hazes were used. The result of haze detection shows that the proba-bility of detecting a haze image is more than 99% and the probability of detecting a haze-free image is 97.9%. Dehazing evaluation results improved the PSNR and SSIM by more than 10% compared to existing networks.
참고문헌 (22)
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