| Summary: | 博士 === 國立臺灣大學 === 電信工程學研究所 === 105 === In this age where everyone can be a photographer with his or her smart phone, the pursuit of higher imaging quality has become more important and profitable than ever before. Among the quality metrics of images, resolution is often the top one that people care the most. Being one of the conventional approaches to increasing the image resolution, optics optimization is believed to have reached its bottleneck. As a consequence, researchers are turning to computational photography to seek breakthrough. In this dissertation, we study the computational approach to high-resolution imaging based on multi-aperture systems such as a camera array or a lenslet array.
This dissertation can be divided into two parts. The first part is dedicated to the analysis of existing approaches. Particularly, two approaches are inspected in depth: subpixel refocusing and reconstruction-based light field super-resolution. For subpixel refocusing, we show that a deconvolution step is missing in previous work and incorporating a deconvolution in the loop significantly enhances the sharpness of the results. We also conduct experiments to quantitatively analyze the effect of calibration error on subpixel refocusing and analyze the upper bound of the error for a targeted image quality. On the other hand, for reconstruction-based light field super-resolution, we show through experiments that the resolution gain obtainable by super-resolution does not increase boundlessly to the number of cameras and is ultimately limited by the size of the point spread function. In addition, we point out through experiment that there is a tradeoff between the obtainable resolution and the registration accuracy. The tradeoff is a fundamental limit of reconstruction-based approaches.
In contrast to the analysis work in the first part, the second part of the dissertation describes our original solution: a computational photography system based on a camera array with mixed focal lengths. Our solution has two distinguished features: it can generate an output image whose resolution is higher than 80% of the total captured pixels and a disparity map of the same resolution that contains the depth information about the scene. Our solution consists of optimized hardware and an image fusion algorithm. On the hardware size, we propose an approach to optimize the configuration of a camera array for high-resolution imaging using cameras with mixed focal lengths and non-parallel optical axes. On the software side, an algorithm is developed to integrate the low-resolution images captured by the proposed camera array into a high-resolution image without the blurry appearance problem of previous methods.
|
|---|
