Depth estimation for underwater images from single view image

Abstract

Underwater images often undergo distortions from scattering, absorption, colour loss, diffraction, polarisation, and varying attenuation depending on the light frequency, due to the water medium. This study answers problems associated with the recovery of underwater images, by developing novel depth map estimation which may be used as an intermediary step for underwater image restoration. The depth map is an important factor for the recovery of the underwater image, as it has been shown that proper estimation of depth, results in better restoration of the underwater image. The proposed algorithm estimates a depth map from a single view image, using blurriness and lighting information, obtained using a simple background neutralisation method. Results from the algorithm on selected raw underwater image data set have been compared with other algorithms and it has been shown that the proposed method gives Pearson coefficients in the range of 0.75-0.95, which are considerably higher and better log root mean square value compared to other methods in the literature. This suggests a better estimation of depth by the proposed method. 10 IET Image Process.

Role of Restored Underwater Images in Underwater Imaging Applications

“…The blurriness map, background light-neutralized image, and intensity of the red channel can then be used for the depth-estimation process [18]. The maximum intensity of the red channel, known as red channel map r(x) of the image, is represented by…”

“…where I r is the intensity of the red channel and ϕ(x) is a square local patch centred at x. The factors used for estimating depth are passed through a stretching function given by Equation ( 10) [18].…”

“…Instead of estimating the transmission map directly, Peng et al [17] use a depth-estimation strategy to restore the underwater images. The proposed method involves the use of depth estimation and transmission map estimation with attenuation coefficient priors, by considering the backscattering effect, and has been proven to show superior results [18].…”

“…The contributions of the paper are: (1) proposing an underwater image restoration method, which estimates depth maps using combinations of a blurriness map, background light neutralization, and red-light intensity. The background light neutralization is estimated using the four-quadrant method, which demands lower computation compared with other methods [18], and (2) demonstrations of the restored underwater image using the proposed method on different underwater applications to evaluate the efficiency of the algorithm. This represents the first ever demonstration of a developed algorithm on real underwater applications.…”

Semantically guided self‐supervised monocular depth estimation

“…The assumption of using calibrated binocular image pairs and multiple observations of a scene of interest excludes itself from utilizing monocular video, which is easier to obtain and richer in variability. To overcome this limitation, several recent works [4,6] have considered the task of depth estimation using a single image or monocular video sequences. Some meaningful monocular cues or features can be exploited to overcome its inherent ambiguity despite the restrictions on monocular depth estimation by ill-posed problems.…”