Multi-objective differential evolution algorithm for underwater image restoration

Underwater image processing area has been considered an important topic within the last decades with important achievements. This kind of images are essentially characterized by their poor visibility because light is exponentially attenuated as it travels in the water and the scenes result poorly contrasted and hazy. On the other hand, image restoration takes into account the influence of the environment on the image in order to achieve an image with an improved quality. This technique consist of inverting the physical model of image formation. That model contains parameters which represent variables such as coefficients of absorption, scattering, among others. In this case, the quality of the restored image depends on the correct estimation of these parameters. In this work, an approach based on evolutionary optimization algorithms is proposed, for restoring underwater images by estimating the model parameters, and using two metrics for quality assessment. The degradation in the images has been simulated by using an image formation model. Results show that image restoration based on a Multi-Objective Differential Evolution (MODE) algorithm achieves images with good contrast and sharpness, being even better than the original image.