A novel feature extraction and mapping using convolutional autoencoder for enhancement of Underwater image/video

Abstract

Marine resources known to human are very limited and as 71% world is surrounded by ocean, we are yet to discover the many of the species and the enriched resources. Often the Underwater scenery collected are poorly illuminated, degraded, and distorted due to light propagation model underwater, water molecules and impurities as well. Counting on to these factors images/videos collected in underwater environment are in need of enhancement. We propose a method of utilizing convolution autoencoder, which can be able to collect the features of underwater images and enhanced image and then the feature mapping of this can be used in testing of the other underwater images/videos. The method utilizes the technique, which combines benefits of unsupervised convolution autoencoder to extract non-trivial features and utilized them for the enhancement of the underwater images. In order to evaluate the performance, we have used both subjective as well as objective evaluation method. Evaluation parameters used represent the results of the proposed method are significant for enhancement of underwater imagery. With the proposed network, we expect to advance underwater image enhancement research and its applications in many areas like in study of marine organism, their behaviour according to the environment, ocean exploration and Autonomous underwater vehicle.