Color Correction Based on CFA and Enhancement Based on Retinex with Dense Pixels for Underwater Images

The main theme of this project is to Enhance and Color Correction for underwater images. This project becomes challenging due to attenuation and scattering of light. In this process, the novel colour correction method based on Color Filter Array (CFA) and an enhancement method based on retinex with dense pixels and adaptive linear histogram transformation for degraded colour-biased underwater images. 

For any digital images, which are captured by digital camera with CFA in the RGB space, their RGB values are dependent and coupled because of the interpolation process. So, we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. 

The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn’t include global light dark relationship of the whole image. So, we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value.

Keywords: Underwater image enhancement, underwater image color correction, color filter array (CFA), Retinex, McCann Retinex, adaptive histogram transform.