Abstract
There are different movements of AUV based on the design and if the AUV glides up and down underwater, so the images captured by the AUV cameras are depth variant images. In this scenario processing and getting high quality images and its information with the less battery power consumption has become a challenge. Recent AUV technology to capture the underwater images demands dedicated hardware unit to obtain clear underwater images without any haze. Since, underwater images are available in different color tones depending on the depth at which images are taken by the AUV cameras requires different processing methods. In this paper, a single hardware unit with Color tone determination prior (CTDP) algorithm is proposed to integrate with AUV’s to process with different color tone images and produce good results. In our proposed image processing method, during the first phase of our work, we determined the color tone using CTDP and restored the red channel. In the second phase, white balancing and image fusion is performed to improve the underwater images for artifact free blending. Our method is experimented on various images of underwater image enhancement benchmark dataset (UIEBD). The results are compared with state-of-art underwater image enhancement methods for different metrics and it is observed that our method maintains the image quality in many benchmark images, it also shows improvement in non-reference metrics UIQM by 6% to 15%, by maintaining proper entropy and UCIQE and full reference metrics PSNR by 5% and SSIM by 11% as compared with previous works. Also, in our paper we proposed the power optimization techniques to be implemented on the proposed hardware unit.
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All data analyzed during this study are included in this published article. The UIEBD dataset [19] is the dataset we have used for our analysis.
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Mungekar, R.P., Jayagowri, R. Color tone determination prior algorithm for depth variant underwater images from AUV’s to improve processing time and image quality. Multimed Tools Appl 82, 31211–31231 (2023). https://doi.org/10.1007/s11042-023-14773-8
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DOI: https://doi.org/10.1007/s11042-023-14773-8