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Modified restoration technique for improved visual perception of shallow underwater imagery

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Images captured underwater often suffer from quality degradation such as low contrast, non-uniform illumination, etc. due to the attenuation and backscattering of light by suspended underwater particles. To overcome this, restoration- cum-enhancement techniques are necessary. Here we present the modified underwater light attenuation prior (MULAP) model using supervised linear regression model to restore the degraded image. The image formation model (IFM)based restoration depends on dual factors: background light and transmission map. Initially, datasets are collected on the close-range point-of-interest. Then, experimental analyses are carried out for those images using the IFM-based methods. For the above techniques, both subjective analysis and objective analysis are done by considering dual metrics such as universal quality index and visual information fidelity factor. Finally, the proposed MULAP shows overwhelming qualitative and quantitative results among other state-of-the-art techniques.

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