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Contrast enhancement of underwater images using conditional generative adversarial network

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Abstract

The scattering effect of light and the presence of water organism deteriorates image quality captured in underwater environment. Researchers have made several proposals to improve the quality of these images using traditional image processing methods. Some of them are specific to underwater images while others are used for generalized purpose. Both these categories can deal with noise, such as explicit modeling, which usually leads to problems of low contrast and color deviation, but are unable to extract image features due to lack of prior knowledge of experts. Recently, machine learning approaches have gained popularity due to its ability to automate image processing task. Also, its efficiency and scalability is good. Therefore, this paper employ Conditional Generative Adversarial Network (CGAN) to synthesize unlabeled images and generate congruent data to original data. The proposed model consists of generator and discriminator. The former maps the features of input image to corresponding high-contrast image while the generated image and real image are passed to the later for the classification purpose. Result analysis illustrates that the proposed framework not only depicts the best Absolute Mean Brightness Error (AMBE), contrast, and Contrast Improvement Index (CII) parameters compared to available mechanisms in the literature but also shows Average Information Content (AIC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Degree of Entropy Un-preservation (DEU) are 99.84%, 99.63%, 97.61% and 98.33% similar to expected outcome. Also, when the technique is compared to the state of art techniques given in literature, the performance is quite good.

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  1. J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

    Archana Agarwal, Shailender Gupta & Munish Vashishath

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  1. Archana Agarwal
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  2. Shailender Gupta
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  3. Munish Vashishath
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Correspondence to Shailender Gupta.

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Agarwal, A., Gupta, S. & Vashishath, M. Contrast enhancement of underwater images using conditional generative adversarial network. Multimed Tools Appl 83, 41375–41404 (2024). https://doi.org/10.1007/s11042-023-17158-z

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