The camera response function (CRF) relates quantised image pixel values with physical incoming light. This paper describes a method to estimate the CRF from a single image of a general two-coloured surface for which the albedo ratio between the coloured regions is known a priori. While other radiometric calibration methods either use multiple frames or require the light to be infinitely distant, the algorithm herein proposed makes no assumptions about lighting conditions and can handle cameras with strong vignetting. Although the approach is generic, in the sense that can be applied to any camera system, the method is particularly well suited for determining the CRF of near-lighting endoscopes in the operating room. This is a very pertinent problem for which no practical, effective solutions have been proposed. The robustness, repeatability, and accuracy of the algorithm is experimentally validated in real images acquired with different endoscopic set-ups.

This work aims to improve the process of speckle noise reduction while preserving edges and other relevant features through filter expansion from 2-D to 3-D. Despeckling is very important for data visual inspection and as a preprocessing step for other algorithms, as they are usually notably influenced by speckle noise. To that intent, a 3-D approach is proposed for the adaptive complex-diffusion filter. This 3-D iterative filter was applied to spectral-domain optical coherence tomography medical imaging volumes of the human retina and a quantitative evaluation of the results was performed to allow a demonstration of the better performance of the 3-D over the 2-D filtering and to choose the best total diffusion time. In addition, we propose a fast graphical processing unit parallel implementation so that the filter can be used in a clinical setting.