Class 1 and Class 2 Underwater Image Enhancement and Restoration Under Turbidity Conditions

Abstract

Poor visibility in underwater images is commonly attributed to the presence of impurities and the absorbed light being scattered while travelling through impure water. In this paper, image enhancement and restoration techniques are applied to TURBID image datasets. The TURBID dataset consists of three different types of underwater image conditions where blue solution, milk solution, or chlorophyll solution is added to water. The images undergo Histogram equalization (HE) and are filtered with a Wiener filter for image enhancement and image restoration, respectively. HE as the chosen enhancement method proved that it could enhance the image quality as the water surface can be seen clearer after enhancement by visual inspection. Three Wiener filter classes are chosen as the restoration method to reduce the Mean Square Error (MSE) value and to get high Peak Signal-to-Noise-Ratio (PSNR) with desired SNR value. Finally, these two image processing techniques, enhancement, and restoration are combined and the image quantitative values are compared to show that the image performance can be improved with combined enhancement and restoration techniques. It is found that Class 1 Wiener Filter with Enhance then Restore (ER) method has a value of 0 for MSE which is the lowest compared to other studied methods and has infinity values for PSNR and SNR.