# Role of Restored Underwater Images in Underwater Imaging Applications
## 1 Introduction  
A considerable part of the earth is covered with water, with the underwater world consisting of an astounding variety of resources. However, the underwater world is not as friendly as the atmospheric region. In order to explore underwater resources and discover the aquatic world, the use of Remotely Operated Vehicles (ROV), such as underwater robots and submarines with proper underwater cameras may be required. The challenges of acquiring undistorted underwater images, which primarily focus on the object of interest, are well-documented due to the distortions caused by marine organisms, floating objects, marine snow, bacteria and algae present in sea water. These sources of distortion make the captured underwater images less informative, and consequently, have limited applicability for some underwater applications, which require undistorted or less distorted images, such as edge detection, feature point detection, image classification, image stitching, object detection, underwater studies, underwater archaeology, marine ecology, assisting aquatic robots, species recognition, and underwater geology. As such, there is a requirement for underwater image capture to have a high degree of accuracy and quality for proper interpretation of its information. Underwater images are commonly dominated by blue and green shades, as red light from the visible spectrum is quickly absorbed and loses its strength, even in the first part of the ocean, which is within 10 m depth. Other lights from the visible spectrum, such as orange, yellow, green, and blue, are also absorbed by the water as we go deeper into the ocean. Figure 1 shows the light absorption property of water and penetration levels of lights [1], at various depths. Due to these problems, a proper method that is able to restoreunderwater images is the need of the hour and is required for various studies and scientific research areas. By gaining more information from the images, the underwater images can be used for different underwater applications. Many restoration algorithms have been proposed in the literature. Reference [2] provides a review of various underwater image restoration methods that are available in the literature, generally classifying underwater restoration methods into hardware, software, and network-based approaches. Hardware-based approaches employ a variety of hardware to process underwater images for restoration purposes. These include range-gated imaging techniques [3], polarisers [4], imaging using stereo cameras [5], and remotely operated vehicles [6]. However, these methods have been shown to suffer from errors caused by calibration of the hardware devices. Network-based approaches involve the use of deep-learning algorithms to process underwater images. Convolutional neural networks [7; 8] and generative adversarial networks [9; 10] are some of the neural networks that have been used for this purpose. However, deep-learning methods commonly require a good dataset with a large number of underwater images, together with ground truth images, which are very difficult to acquire in the case of underwater image processing. Software-based approaches use the Image Formation Model (IFM) to restore the captured underwater images, by finding the background light and transmission maps. Dark Channel Prior (DCP), as proposed by [PERSON] et al. [11], uses IFM in image restoration, by assuming scene points closer to the camera as dark images and vice versa. However, due to the longer wavelength and faster attenuation property of red light, the method fails to estimate the proper results, and always ends up choosing the red channel as the darkest of all channels. Variations of DCP have consequently been proposed for underwater images; using green and blue channels only, [12; 13; 14], using the inverse of the red channel [15], and using the maximum intensity prior [16]. The performances of these methods have been shown to vary depending on different lighting conditions and priors chosen.