Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended

Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution

Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images

Automatic Image Based Time Varying 3D Feature Extraction and Tracking

3D time-varying data sets are complex. The intrinsics of those data cannot be readily comprehended by users solely based on visual investigation. Computational tools such as feature extraction and tracking are often necessary. Until now, most existing algorithms in this domain work effectively in the object space, relying on prior knowledge of the data. How to find a more flexible and efficient method which can perform automatically to implement extraction and tracking remains an attractive topic. This thesis presents a new image-based method that extracts and tracks the 3D time- varying volume data sets. The innovation of the proposed approach is two-fold. First, all analyses are performed in the image space on volume rendered images without accessing the actual volume data itself. The image-based processing will help to both save storage space in the memory and reduce computation burden. Secondly, the new approach does not require any prior knowledge of the user-defined “feature” or a built model. All the parameters used by the algorithms are automatically determined by the system itself, thus flexibility and efficiency can be achieved at the same time. The proposed image-based feature extraction and tracking system consists of four components: feature segmentation (or extraction), feature description (or shape analysis), classification, and feature tracking. Feature segmentation is to identify and label individual features from the image so that we can describe and track them separately. We combine both region-based and edge-based segmentation approaches to implement the extraction process. Feature description is to analyze each feature and derive a vector to describe the feature such that the subsequent tracking step does not have to rely on the entire feature extracted, but instead a much smaller and informative feature descriptor. Classification is to identify the corresponding features from two consecutive image frames along both the time and the spatial domain. Feature tracking is to study and model the evolution of features based on the correspondence computation result from classification stage. Experimental results show that the image-based feature extraction and tracking system provides high fidelity with great efficiency

SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art