Depth-From-Defocus (DFD) is a monocular computer vision technique for creating\ud depth maps from two images taken on the same optical axis with different intrinsic camera\ud parameters. A pre-processing stage for optimally converting colour images to monochrome\ud using a linear combination of the colour planes has been shown to improve the\ud accuracy of the depth map. It was found that the first component formed using Principal\ud Component Analysis (PCA) and a technique to maximise the signal-to-noise ratio (SNR)\ud performed better than using an equal weighting of the colour planes with an additive noise\ud model. When the noise is non-isotropic the Mean Square Error (MSE) of the depth map\ud by maximising the SNR was improved by 7.8 times compared to an equal weighting and\ud 1.9 compared to PCA. The fractal dimension (FD) of a monochrome image gives a measure\ud of its roughness and an algorithm was devised to maximise its FD through colour\ud mixing. The formulation using a fractional Brownian motion (mm) model reduced the\ud SNR and thus produced depth maps that were less accurate than using PCA or an equal\ud weighting. An active DFD algorithm to reduce the image overlap problem has been\ud developed, called Localisation through Colour Mixing (LCM), that uses a projected colour\ud pattern. Simulation results showed that LCM produces a MSE 9.4 times lower than equal\ud weighting and 2.2 times lower than PCA.\ud The Point Spread Function (PSF) of a camera system models how a point source of\ud light is imaged. For depth maps to be accurately created using DFD a high-precision PSF\ud must be known. Improvements to a sub-sampled, knife-edge based technique are presented\ud that account for non-uniform illumination of the light box and this reduced the\ud MSE by 25%. The Generalised Gaussian is presented as a model of the PSF and shown to\ud be up to 16 times better than the conventional models of the Gaussian and pillbox
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