11/8/2023 0 Comments Visual enhancement![]() ![]() IEEE Trans Image Process 25(11):5187–5198Ĭhen Z, Wang H, Shen J, Li X, Xu L (2014) Region-specialized underwater image restoration in inhomogeneous optical environments. Multimed Tools Appl 78(2):1649–1683Ĭai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: An end-to-end system for single image haze removal. Trends Ecol Evol 22(3):148–155īoujelbene R, Jemaa YB, Zribi M (2019) A comparative study of recent improvements in wavelet-based image coding schemes. Signal Process Image Commun 89:115978īickford D, Lohman DJ, Sodhi NS, Ng PK, Meier R, Winker K, Ingram KK, Das I (2007) Cryptic species as a window on diversity and conservation. IEEE Trans Image Process 1(2):205–220Īnwar S, Li C (2020) Diving deeper into underwater image enhancement: a survey. IEEE Trans Image Process 27(1):379–393Īntonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using wavelet transform. IEEE Trans Image Process 22(8):3271–3282Īncuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2018) Color balance and fusion for underwater image enhancement. In: MTS/IEEE Monterey OCEANS, pp 1–5Īncuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. J Mar Sci Technol 22(4):758–770Īlex RS, Deepa S, Supriya M (2016) Underwater image enhancement using clahe in a reconfigurable platform. IEEE Trans Intell Transp Syst 18(7):1907–1917Īhn J, Yasukawa S, Sonoda T, Ura T, Ishii K (2017) Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition. CRC Press, Boca Raton, Florida, USAĪgarwal S, Regentova EE, Kachroo P, Verma H (2017) Multidimensional compression of its data using wavelet-based compression techniques. IET Image Process 13(10):1587–1596Īddison PS (2017) The illustrated wavelet transform handbook: Introductory theory and applications in science, engineering, medicine and finance. Four evaluation measures, exclusively designed for underwater scenarios (underwater image quality, contrast, sharpness, and colorfulness measures) are also used to test the performance of the proposed scheme.Ības PE, De Silva LC et al (2019) Review of underwater image restoration algorithms. Encouraging scores of 35.01, 30.105, 27.22, 30.10, and 27.8, are achieved for the BIQI, BRISQUE, SSEQ, DIIVINE, and BLIINDS, respectively. The quantitative evaluation has been carried out using five well established general purpose non-reference image quality indices namely BIQI (blind image quality index), BLIINDS (BLind Image Integrity Notator using DCT Statistics), DIIVINE (Distortion Identification-based Image Verity and INtegrity Evaluation), BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator), and SSEQ (Spatial-Spectral Entropy-based Quality). The effectiveness of the proposed scheme is justified by comparing it with different state-of-the-art image dehazing techniques. The proposed scheme is tested with numerous standard underwater images, as well as the excavation images of Dwaraka (Dvārakā) underwater ruins. The third stage is used for reconstruction, where the enhanced image details are used along with the single level approximate of the input image to get the dehazed underwater image using inverse discrete wavelet transform. ![]() In the second stage, the negative of detail images are enhanced by the help of dark channel prior. In the first stage, image details are extracted using discrete wavelet transform followed by image negative operation. ![]() A three-fold scheme for dehazing of underwater sequences is proposed. In this article, we have exploited the multi-resolution ability of discrete wavelet transform and applied dark channel prior based transmission map estimation scheme to dehaze the highly degraded underwater image and restored the color. The scene visibility reduces to a great extent due to haze, which makes the underwater visual surveillance quite a challenging task. Haze is an obvious phenomenon in the underwater scenario. ![]()
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