Table 1 Brief description of the algorithms.
From: Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods
Algorithm | Model's characteristics and assumptions | Experiments and data set | Image quality evaluation |
|---|---|---|---|
Jaffe [2] 1990 | Computer modeling. Image as linear superposition of direct, forward and scattered components. Essentially for artificial lit scenes. | Simulation and utility of different imaging and lighting configurations are evaluated. | Visual inspection. |
Image restoration methods | |||
Measurement of PSF of water and automated restoration scheme. Natural and artificial lighting. Blurring caused by strong scattering due to water and floating particles. | Two water types (clear and turbid), morning and afternoon. Target between 3.7 and 7.1 m. | Visual inspection. Image quality metric: Weighted Gray Scale Angles (WGSA). | |
Trucco and Olmos [13] 2006 | Self tuning restoration filter. Uniform illumination Only forward scatter is considered. Limited backscatter. | Ocean images in shallow water, direct sunlight illumination. Some images with high backscatter. | Visual inspection. Quantitative tests on frames from real mission videos. Improvement to classification tasks for subsea operations (detecting man-made objects on the seafloor). |
Liu et al. [16] 2001 | Measurement of PSF of water and image restoration. Standard and parametric Wiener filter deconvolution. | Measurements on controlled environment. Set up: light source, slit images at 1–3 m in water tank. Restoration of images taken in turbid water. | Visual inspection. |
Schechner and Karpel [17] 2005 | Polarization associated with the prime visibility disturbance to be deleted (backscatter). Natural lighting. | Polarizer used to analyze the scene. Experiments in the sea (scene 26 m deep). | Visual inspection. Quantitative estimate for the visibility improvement. Estimation of the distance map of the scene. |
Treibitz and Schechner [19] 2009 | Polarization-based method for visibility enhancement and distance estimation in scattering media. Artificial illumination. | Experiments in real underwater scenes: Mediterranean sea, Red Sea and lake of Galilee. | Visual inspection. Quantitative estimate for the visibility improvement. |
Image enhancement and color correction methods | |||
Bazeille et al. [20] 2006 | Automatic pre-processing. Natural and artificial illumination. | Deep marine habitats. Scenes with man-made objects in the sea floor. | Visual inspection. Quantitative index: closeness of histogram to exponential distribution and tests for object recognition in the sea floor. |
Chambah et al. [23] 2004 | Underwater color constancy. Artificial lighting. | Images taken in aquariums. Tests on fish segmentation and fish recognition. | Visual inspection. |
Iqbal et al. [25] 2007 | Enhancement based on slide stretching. Natural and artificial illumination. | Marine habitats. | Visual inspection and histogram analysis. |
Automatic free denoising. Backscatter is considered as the first noise. Adaptive smoothing filter. Natural and artificial lighting. | Marine habitats with unknown turbidity characteristics. | Visual inspection. Quantitative criteria based on closeness of histogram to exponential distribution. | |
Torrez-Mendez and Dudek [27] 2005 | Color recovery using an energy minimization formulation. Natural and artificial lighting. | Training data set: marine habitats (ground truth is known) and frames from videos in the deep ocean (no ground truth available). | Residual error is computed between ground truth and corrected images. |
Ahlen et al. [28] 2007 | Hyperspectral data for color correction. Natural illumination. | Test image: colored plate at 6 m depth in the sea. Coral reefs and marine habitats. | Visual inspection. |
Petit et al. [29] 2009 | Enhancement method: color space contraction using quaternions. Natural and artificial lighting | Marine habitats at both shallow and deep waters. | Visual inspection. |
Garcia et al. [30] 2002 | Compensating for lighting problems: non uniform illumination. | Shallow waters on a sunny day. Shallow waters at sun down (simulating deep ocean). | Visual inspection. |
Arredondo and Lebart [39] 2005 | Video processing algorithms. Simulations of perturbations. Natural and artificial lighting | Test images are degraded with simulated perturbations. Simulations in shallow (1–7 m) and deep waters. | Visual inspection. Quantitative evaluation: mean angular error is measured in motion estimation for different methods as a function of Gaussian noise. |