Köhler T (2017)
Publication Language: English
Publication Type: Thesis
Publication year: 2017
Pages Range: 253
URI: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/9145
The optical resolution of a digital camera is one of its most crucial parameters with broad relevance for consumer electronics, surveillance systems, remote sensing, or medical imaging. However, resolution is physically limited by the optics and sensor characteristics. In addition, practical and economic reasons often stipulate the use of out-dated or low-cost hardware. Super-resolution is a class of retrospective techniques that aims at high-resolution imagery by means of software. Multi-frame algorithms approach this task by fusing multiple low-resolution frames to reconstruct high-resolution images. This work covers novel super-resolution methods along with new applications in medical imaging.
The first contribution of this thesis concerns computational methods to super-resolve image data of a single modality. The emphasis lies on motion-based algorithms that are derived from a Bayesian statistics perspective, where subpixel motion of low-resolution frames is exploited to reconstruct a high-resolution image. More specifically, we introduce a confidence-aware Bayesian observation model to account for outliers in the image formation, e.g. invalid pixels. In addition, we propose an adaptive prior for sparse regularization to model natural images appropriately. We then develop a robust optimization algorithm for super-resolution using this model that features a fully automatic selection of latent hyperparameters. The proposed approach is capable of meeting the requirements regarding robustness of super-resolution in real-world systems including challenging conditions ranging from inaccurate motion estimation to space variant noise. For instance, in case of inaccurate motion estimation, the proposed method improves the peak-signal-to-noise ratio (PSNR) by 0.7 decibel (dB) over the state-of-the-art.
The second contribution concerns super-resolution of multiple modalities in the area of hybrid imaging. We introduce novel multi-sensor super-resolution techniques and investigate two complementary problem statements. For super-resolution in the presence of a guidance modality, we introduce a reconstruction algorithm that exploits guidance data for motion estimation, feature driven adaptive regularization, and outlier detection to reliably super-resolve a second modality. For super-resolution in the absence of guidance data, we generalize this approach to a reconstruction algorithm that jointly super-resolves multiple modalities. These multi-sensor methodologies boost accuracy and robustness compared to their single-sensor counterparts. The proposed techniques are widely applicable for resolution enhancement in a variety of multi-sensor vision applications including color-, multispectral- and range imaging. For instance in color imaging as a classical application, joint super-resolution of color channels improves the PSNR by 1.5 dB compared to conventional channel-wise processing.
The third contribution transfers super-resolution to workflows in healthcare. As one use case in ophthalmology, we address retinal video imaging to gain spatio-temporal measurements on the human eye background non-invasively. In order to enhance the diagnostic usability of current digital cameras, we introduce a framework to gain high-resolution retinal images from low-resolution video data by exploiting natural eye movements. This framework enhances the mean sensitivity of automatic blood vessel segmentation by 10 % when using super-resolution for image preprocessing. As a second application in image-guided surgery, we investigate hybrid range imaging. To overcome resolution limitations of current range sensor technologies, we propose multi-sensor super-resolution based on domain-specific system calibrations and employ high-resolution color images to steer range super-resolution. In ex-vivo experiments for minimally invasive and open surgery procedures using Time-of-Flight (ToF) sensors, this technique improves the reliability of surface and depth discontinuity measurements compared to raw range data by more than 24 % and 68 %, respectively.
APA:
Köhler, T. (2017). Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging (Dissertation).
MLA:
Köhler, Thomas. Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging. Dissertation, 2017.
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