ENHANCEMENT OF UNDERWATER IMAGES WITH ARTIFICIAL INTELLIGENCE


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Ertan Z., Korkut B., Gördük G., KULAVUZ B., BAKIRMAN T., BAYRAM B.

8th International Conference on GeoInformation Advances, GeoAdvances 2024, İstanbul, Turkey, 11 - 12 January 2024, vol.48, pp.149-156 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 48
  • Doi Number: 10.5194/isprs-archives-xlviii-4-w9-2024-149-2024
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.149-156
  • Keywords: Deep Learning, Generative Adversarial Networks, Image Enhancement, Pix2Pix, Underwater Image
  • Yıldız Technical University Affiliated: Yes

Abstract

Camera systems using optical sensors have made great progress in recent years in obtaining underwater images. Very high-resolution images can be obtained with underwater cameras. However, it becomes difficult to process the image obtained due to many distorting factors such as inhomogeneous underwater lighting, low contrast, blur, sea snow. For this purpose, image enhancement algorithms are used to minimise the problems in the captured images. The physical characteristics of the underwater environment cause degradation effects that are not found in normal images captured in air. Due to the effects such as colour distortion, low contrast and brightness, blurred details in images captured in this type of environment, the usage area of these images is very limited. This project aims to use artificial intelligence methods for colour enhancement of underwater images. In addition, one of the main objectives of the project is to examine the effect of colour enhancement applied to images on other areas of use. Within the scope of the project, An Underwater Image Enhancement Benchmark Dataset and Beyond Dataset and Large Scale Underwater Image Dataset datasets were used. In our study, U-Shape Transformer architecture and Pix2Pix architecture were tested for their usability for image enhancement. According to the results obtained, Pix2Pix architecture achieved the highest accuracy. Accuracy results were obtained as 22.5884 and 0.8764 for PSNR and SSIM, respectively using UIEB dataset. The accuracy values for LSUI dataset are 25.1010 and 0.8440 for PSNR and SSIM, respectively.