This thesis explores underwater imaging solutions in order to alleviate the poor contrast and the distortion in the perception of colour caused by the processes of scattering and absorption. We demonstrate through simulated experiments that imaging systems with higher spectral resolution than RGB could be useful for underwater imaging tasks such as estimating illumination and spectral reflectance values. We also tested hyperspectral imagers in real world experiments and found that the current technology is limited for underwater image enhancement applications. To address the problem of poor visibility in underwater scenes we introduce dehazing methods for underwater RGB images and videos. Current underwater dehazing methods suffer from limitations such as estimated parameters being biased towards pixels of bright objects in a scene and artefacts being created in regions that contain pure haze. Bright objects in a scene are avoided by using texture features during the estimation of parameters and local bias is avoided by taking information from an image at different spatial resolutions. We inhibit noise and artefacts being created in the pure haze regions by segmenting these areas and treating them as a special case. We address the spectral distortion present in underwater scenes by applying a water-type dependent white balancing step. We also demonstrate the application of our method to underwater videos with a weighted temporal smoothing of the estimated parameters and a Gaussian normalisation step that ensures segmentation of pure haze regions is stable across frames. We evaluate our methods both on quantitative metrics and through subjective experiments and demonstrate an improved performance in comparison to the state of the art in underwater image and video enhancement. We also show how no-reference underwater image quality assessment metrics do not always correspond with human judgement and provide suggestions on how they could be improved.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:766016 |
| Date | January 2017 |
| Creators | Emberton, Simon |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://qmro.qmul.ac.uk/xmlui/handle/123456789/29605 |
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