Underwater Image Dehazing in YCbCr Color Space Using Superpixel Segmentation
Mrinmoy Kumar Das, Nitya Varshini Gaddala, Pratinav Reddy Bommireddy, Rahul Roy · 2023
The article introduces a novel technique to improve underwater image quality, addressing challenges such as light absorption, poor contrast, scattering effects, and color distortions. The algorithm uses the YCbCr color space to separate image luminance and chrominance, allowing a targeted treatment of these elements. It begins by converting the RGB underwater image to YCbCr, followed by decomposing it into luminance and chrominance components. The luminance undergoes dehazing using the underwater normalized total variation (UNTV) method and superpixel segmentation to improve clarity and reduce haze. Atmospheric light estimation through superpixel segmentation contributes to accurate scene radiance restoration. Chrominance is then refined using the dehazed luminance, enhancing color accuracy and contrast. After independent processing, the luminance and chrominance components are recombined in the YCbCr space, and the final image is converted back to RGB. The efficacy of the algorithm is demonstrated on two benchmark datasets, showing its promise and highlighting the potential of the YCbCr color space for effective underwater image enhancement.