Author: Raquel Gil Rodríguez

Publisher:

Published: 2018

Total Pages: 172

ISBN-13:

In this thesis we focus on two different image processing challenges: high dynamic range (HDR) image/video generation, and colour matching. In both cases, we redefine these tasks by taking into account the prior knowledge from the different processes the camera performs when capturing the image. High dynamic range techniques have recently become very popular, thanks to the emergence of HDR recording and displaying technology. We propose two different approaches for HDR creation, one for still images, and one for video creation. In case of still images, most of previous methods follow a multiple exposure combination approach. These methods share a set of building assump-tions: i) the full dynamic range of the scene can be recovered, ii) colour channels are independent, and iii) the camera response function remains constant while varying the exposure time. First, we highlight how these assumptions do not apply in general for digital cameras, and then, we propose a method to im-prove multiple exposure combination. Our results outperform state-of-the-art multiple exposure techniques.In case of HDR video, we present a simple and affordable method to gen-erate high quality video from an HDR scene. Our input is an interlaced video alternating row pairs with different ISO values, as some DSLR camera models can provide. The proposed algorithm involves two main steps: i) the compu-tation of two single-ISO full-frame images (one for each ISO value) using an inpainting-based deinterlacing method, and ii) the linear combination of the full-frame ISO's into a single HDR frame. Finally, the results are tone-mapped into an LDR frame ready for display. Results are free of ghosting artefacts and present little noise.Colour matching approaches attempt to transfer the colours of a reference image, to another source image. In this context, we focus on colour matching two images taken from the same scene. First, we propose a method that mod-ifies logarithmic encoded images, used in cinema for HDR content, in order to behave as gamma encoded images, used in most digital cameras. Then, we ex-tend a previous approach defined just for gamma encoded images. We redefine the transformation between the two images, by considering a projective trans-form, and we estimate the method parameters in a single optimisation step. The method outperforms the state-of-the-art methods and can handle real-life examples.