SU ALTI GÖRÜNTÜ İYİLEŞTİRMEDE KULLANILAN ALGORİTMALARIN KARŞILAŞTIRILMASI

Yıl 2024, , 33 - 45, 25.03.2024

Birkan Büyükarıkan

https://doi.org/10.57120/yalvac.1388877

Öz

Su altının keşfi, son yıllarda ilginç bir araştırma konusu olmuştur. Ancak su altı ortamındaki bulanıklık, renk dağılımı ve kontrast gibi etkenler, su altı görüntülerinde gürültü ve ayrıntı kaybı gibi bozulmalara neden olur. Bu durum, su altı görüntü analizi uygulamalarında karşılaşılan zorlukları arttırır. Bu sorunların üstesinden gelebilmek için görüntü iyileştirme algoritmaları kullanılabilir. Bu çalışmanın amacı, mevcut görüntü iyileştirme algoritmaları yardımıyla su altı görüntülerinin geliştirilmesidir. Çalışmada mevcut iyileştirme algoritmalarından; tek-ölçekli retineks (SSR), çok ölçekli retineks (MSR), renk düzeltmeli çok ölçekli retineks (MSRCR), öncelikli histogram dağıtımı (DHDP) ve çok ölçekli ilişkili dalgacık (MSCW) kullanılmıştır. Çalışma, gerçek dünya verilerini içeren bir veri setinden seçilen görüntülere uygulanmıştır. Kullanılan iyileştirme algoritmalarının performanslarını göstermek için tam referanslı ve referanssız ölçütlerle değerlendirme yapılmıştır. Seçilen görüntülerin değerlendirme ölçütlerinden elde edilen sonuçlara göre MSRCR algoritmasıyla iyileştirilen görüntülerde ortalama olarak daha iyi sonuçlar elde edilmiştir. MSRCR algoritmasının tepe sinyal-gürültü oranı, yapısal benzerlik indeksi, kör/referanssız görüntü uzamsal kalite değerlendiricisi, doğallık görüntü kalitesi değerlendiricisi, algı tabanlı görüntü kalitesi değerlendiricisi, su altı görüntü kalitesi ölçütü ve su altı renkli görüntü kalitesi değerlendirme ölçütlerinin ortalama puanları sırasıyla 15,6454, 0,4516, 22,6035, 6,4106, 34,7032, 1,7344 ve 7,776’dır. Deneysel sonuçlar, su altı görüntülerinde bozulmaları azaltmak için görüntü iyileştirme yöntemlerinin kullanılabilirliğini göstermektedir.

Anahtar Kelimeler

Su Altı Görüntüler, Görüntü İyileştirme, Görüntü Kalitesi

COMPARISON OF ALGORITHMS USED IN UNDERWATER IMAGE ENHANCEMENT

Öz

The exploration of the underwater world has become an intriguing research subject in recent years. However, factors such as blurriness, color distribution, and contrast in the underwater environment lead to distortions such as noise and loss of detail in underwater images. This situation increases the challenges encountered in underwater image analysis applications. Image enhancement algorithms can be employed to overcome these problems. The aim of this study is to improve underwater images using existing image enhancement algorithms. Single Scale Retinex (SSR), Multi Scale Retinex (MSR), Multi Scale Retinex with Color Restoration (MSRCR), Dehazing Histogram Distribution Prior (DHDP), and Multi-Scale Correlated Wavelet (MSCW) are among the enhancement algorithms used in this study. This study was applied to images selected from a data set containing real-world data. Evaluations with both full-reference and no-reference metrics were conducted to demonstrate the enhancement algorithms' performance. According to the results obtained from the evaluation metrics of the selected images, images enhanced with the MSRCR algorithm generally achieved better results on average. The average scores for the MSRCR algorithm in Peak Signal-to-Noise Ratio, Structural Similarity Index, Blind/referenceless Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, Perception based Image Quality Evaluator, Underwater Image Quality Measure, and Underwater Color Image Quality Evaluation, underwater image quality criterion, and underwater colored image quality evaluation criteria are 15.6454, 0.4516, 22.6035, 6.4106, 34.7032, 1.7344, and 7.776, respectively. Experimental results demonstrate the effectiveness of image enhancement methods in reducing distortions in underwater images.

Anahtar Kelimeler

Underwater Images, Image Enhancement, Image Quality

Kaynakça

• [1] Guo Y, Li H, Zhuang P, (2019) Underwater image enhancement using a multiscale dense generative adversarial network. IEEE Journal of Oceanic Engineering, 45 (3): 862-870.
• [2] Hu K, Zhang Y, Lu F, Deng Z, Liu Y, (2020) An underwater image enhancement algorithm based on MSR parameter optimization. Journal of Marine Science and Engineering, 8 (10): 741.
• [3] Zhou J, Yao J, Zhang W, Zhang D, (2022) Multi-scale retinex-based adaptive gray-scale transformation method for underwater image enhancement. Multimedia Tools and Applications: 1-21.
• [4] Quan X, Wei Y, Li B, Liu K, Li C, Zhang B, Yang J, (2022) The Color Improvement of Underwater Images Based on Light Source and Detector. Sensors, 22 (2): 692.
• [5] Land EH, McCann JJ, (1971) Lightness and retinex theory. Josa, 61 (1): 1-11.
• [6] Jobson DJ, Rahman Z-u, Woodell GA, (1997) Properties and performance of a center/surround retinex. IEEE transactions on image processing, 6 (3): 451-462.
• [7] Jobson DJ, Rahman Z-u, Woodell GA, (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing, 6 (7): 965-976.
• [8] Muniraj M, Dhandapani V, (2021) Underwater image enhancement by combining color constancy and dehazing based on depth estimation. Neurocomputing, 460: 211-230.
• [9] Zhang W, Dong L, Xu W, (2022) Retinex-inspired color correction and detail preserved fusion for underwater image enhancement. Computers and Electronics in Agriculture, 192: 106585.
• [10] Zhao J-l, Chen Z-q, Jiang H-y, Zhang Q, (2023) Deep Retinex image enhancement algorithm under weak Light Conditions. 2023 IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 857-861.
• [11] Katırcıoğlu F, (2021) Düşük-Işıklı Renkli Görüntülerin İyileştirilmesinde Kullanılan Retineks Algoritmalarının Karşılaştırmalı Analizi. Mühendislik Bilimleri ve Araştırmaları Dergisi, 3 (2): 188-206.
• [12] Badrinarayanan V, Kendall A, SegNet RC, (2015) A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:151100561, 5.
• [13] Li C-Y, Guo J-C, Cong R-M, Pang Y-W, Wang B, (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Transactions on Image Processing, 25 (12): 5664-5677.
• [14] Liu X, Zhang H, Cheung Y-m, You X, Tang YY, (2017) Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach. Computer Vision and Image Understanding, 162: 23-33.
• [15] Tajeripour F, Fekri-Ershad S, (2014) Developing a novel approach for stone porosity computing using modified local binary patterns and single scale retinex. Arabian Journal for Science and engineering, 39: 875- 889.
• [16] Pazhani AAJ, Periyanayagi S, (2022) A novel haze removal computing architecture for remote sensing images using multi-scale Retinex technique. Earth Science Informatics, 15 (2): 1147-1154.
• [17] Gao Z, Zhai Y, (2022) Image Dehazing Based on Multi-scale Retinex and Guided Filtering. 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 123-126.
• [18] Li D, Sun J, Wang H, Shi H, Liu W, Wang L, (2022) Research on haze image enhancement based on dark channel prior algorithm in machine vision. Journal of Environmental and Public Health, 2022.
• [19] Li C, (2016) TIP2016-code, https://github.com/Li-Chongyi/TIP2016-code:
• [20] Liu X, (2017) waveletdehaze-sourcecode-v1.0, https://github.com/starxliu/waveletdehaze-sourcecode-v1.0:
• [21] Chen X, (2020) PSNR-SSIM-UCIQE-UIQM-Python, https://github.com/xueleichen/PSNR-SSIM-UCIQE-UIQM-Python/blob/main/evaluate.py:
• [22] Chen X, Li J, Hua Z, (2023) Retinex low-light image enhancement network based on attention mechanism. Multimedia Tools and Applications, 82 (3): 4235-4255.
• [23] Mittal A, Moorthy AK, Bovik AC, (2012) No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing, 21 (12): 4695-4708.
• [24] John Chemmanam A, Jose BA, (2023) Fused features for no reference image quality assessment. The Imaging Science Journal: 1-13.
• [25] Yang M, Sowmya A, (2015) An underwater color image quality evaluation metric. IEEE Transactions on Image Processing, 24 (12): 6062-6071.
• [26] Srinivas S, Siddharth VR, Dutta S, Khare NS, Krishna L, (2022) Channel prior based Retinex model for underwater image enhancement. 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 1-10.
• [27] Panetta K, Gao C, Agaian S, (2015) Human-visual-system-inspired underwater image quality measures. IEEE Journal of Oceanic Engineering, 41 (3): 541-551.
• [28] Nordølum BJ, Lavik EO, Haugen KAD, Kvalvaag T-RT, (2021) Artsgjenkjenning av fisk, NTNU.
• [29] Kimmel R, Elad M, Shaked D, Keshet R, Sobel I, (2003) A variational framework for retinex. International Journal of computer vision, 52: 7-23.