Füzyon Tabanlı Hibrit Sis Giderme

Abstract

Yaşadığımız ortamda bulunan aerosoller, çektiğimiz görüntülerin kalitesini azaltmaktadır. Farklı amaçlar için, elde edilen görüntülerdeki bulanıklıkların temizlenmesi ihtiyaç duyulmaktadır. Bunu gerçekleştirmek için literatürde çok sayıda algoritma bulunmaktadır. Son 10 yılda sis giderme görüntü işleme probleminde birçok yeni yaklaşım geliştirilmiştir. Bunlarda, en başarılı olan ve en çok kullanılan algoritma Dark Channel Prior algoritmasıdır. Dark Channel Prior algoritması, farklı renk kanallarında çok düşük piksel yoğunluğu değerlerine dayanmaktadır. Bu düşük yoğunluklu değerler, algoritma ile görüntüdeki sis için bir yama oluşturur ve sisli sahneleri kaldırabilir veya etkisini azaltabilir. Bu çalışmada, “exposure fusion” algoritmasını kullanarak, birbirinden farklı pozlamaları contrast ve doygunluk gibi değerlere göre füzyon ederek daha iyi pozlanmış imgeler elde edilmiştir. Bu bildiride, Dark Channel Prior algoritmasının, exposure fusion algoritması ile beraber kullanılması ile, daha başarılı sis giderme sonuçlarının elde edildiği farklı örneklerle gösterilecektir.

Keywords

• Görüntü İşleme
• Sis Kaldırma
• Görüntü Onarma
• Kontrast İyileştirme
• Dark Channel Prior Algoritması
• Exposure Fusion Algoritması

Fusion-Based Hybrid Fog Removal

Abstract

Aerosols in the environment we live in reduce the quality of the images we take. For different purposes, it is necessary to clean the blurs in the obtained images. There are many algorithms in the literature to achieve this. In the last 10 years, many new approaches have been developed in the problem of defogging image processing. Among these, the most successful and most used algorithm is the Dark Channel Prior algorithm. The Dark Channel Prior algorithm is based on very low pixel density values in different color channels. These low density values create a patch for the fog in the image with the algorithm and can remove or reduce the effect of foggy scenes. In this study, by using the "exposure fusion" algorithm, better exposed images were obtained by fusing different exposures according to values such as contrast and saturation. In this paper, it will be shown with different examples that more successful fog removal results are obtained by using the Dark Channel Prior algorithm together with the exposure fusion algorithm.

Keywords

• Image Processing
• Fog Removal
• Image Restoration
• Contrast Enhancement
• Dark Channel Prior Algorithm
• Exposure Fusion Algorithm

References

1. Kaiming He, Jian Sun, & Xiaoou Tang. “Single image haze removal using dark channel prior”. IEEE 2009 Conference on Computer Vision and PatternRecognition, 2009.
2. Narasimhan, Srinivasa G. and Shree K. Nayar. “Chromatic framework for vision in bad weather.” IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000, 598-605, 2000.
3. Huang, S.C., Chen, B.H., Wang, W.J. “Visibility restoration of single hazy images captured in real-world weather conditions”. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1814–1824, 2014.
4. Tripathi, A., Mukhopadhyay, S. “Single image fog removal using anisotropic diffusion”. IET Image Process, 6(7), 966–975, 2012.
5. He, K., Sun, J., Tang, X. “Guided image filtering”. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409, 2013.
6. Li, Z., Zheng, J. “Edge-preserving decomposition-based single image haze removal”. IEEE Trans. Image Process. 24(12), 5432– 5441, 2015.
7. M. Bertalmío, Image Processing for Cinema, 1st Edition, Chapman and Hall/CRC, Boca Raton, 2014
8. A. Galdran, “Image Dehazing by Artificial Multiple-Exposure Image Fusion”. Signal Processing, 135-147, 2018.

9. P. J. Burt, “The Pyramid as a Structure for Efficient Computation”. Multiresolution Image Processing and Analysis, Springer Series in Information Sciences, Springer, Berlin, Heidelberg, 6–35, 1984.
10. P. J. Burt, R. J. Kolczynski, “Enhanced image capture through fusion”. (4th) International Conference on ComputerVision, 1993, 173–182.
11. T. Mertens, J. Kautz, F. V. Reeth, “Exposure Fusion”. 15th Pacific Conference on Computer Graphics and Applications, PG ’07, 382–390, 2007.
12. Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C. “Ohaze: a dehazing benchmark with real hazy and haze-free outdoor images”. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 754–762, 2018.
13. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. “Image quality assessment: from error visibility to structural similarity”. IEEE Trans. Image Process. 13(4), 600–612, 2004.
14. Mittal A, Moorthy AK, Bovik A.C. “No-reference image quality assessment in the spatial domain”. IEEE Trans Image Process 21(12), 4695–4708, 2012.