3D Information from Scattering Media Images

Scattering media environments are real-world conditions that occur often, in daily life. Some examples of scattering media are haze, fog, and other bad weather conditions. In these environments, micro-particles in the surrounding media interfere with light propagation and image formation. Thus, images that are captured in these scattering media environments will suffer from low contrast and loss of intensity. This becomes an issue for computer vision methods that employ features found in the scene. To solve this issue, many approaches must estimate the corresponding clear scene prior to further processing. However, the image formation model in scattering media shows potential 3D distance information about the scene encoded implicitly in image intensities. In this paper, we investigate the potential information that can be extracted directly from the scattering media images. We demonstrate the possibility of extracting relative depth in the form of transmission as well as explicit depth maps from single images

Contrast Enhancement for Images in Turbid Water

Absorption, scattering, and color distortion are three major degradation factors in underwater optical imaging. Light rays are absorbed while passing through water, and absorption rates depend on the wavelength of the light. Scattering is caused by large suspended particles, which are always observed in an underwater environment. Color distortion occurs because the attenuation ratio is inversely proportional to the wavelength of light when light passes through a unit length in water. Consequently, underwater images are dark, low contrast, and dominated by a bluish tone. In this paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a robust color lines-based ambient light estimator and a locally adaptive filtering algorithm for enhancing underwater images in shallow oceans. Furthermore, we propose a spectral characteristic-based color correction algorithm to recover the distorted color. The enhanced images have a reasonable noise level after the illumination compensation in the dark regions, and demonstrate an improved global contrast by which the finest details and edges are enhanced significantly