Traditional systems for image creation and enhancement are completely reliant on user input, either to generate content directly or to evaluate and tune algorithms. The input must be generated by talented artists, and it is applicable only to the image for which it was originally intended. This poses scalability challenges for such traditional image generation and editing systems.
An alternative paradigm, which is being researched in a variety of content-generation domains, is to exploit the large amount of data made available by users over the Internet. This data may take many forms: images, image keywords, and even judgments about image quality. Although the data is noisy and was not originally intended for the task at hand, it nevertheless serves as a useful source of "prior information." These priors make it possible to build image creation and enhancement systems that require only a small amount of user input, and do not require the users to be professional artists or designers.
This thesis describes three such systems, including low-level image-processing (motion deblurring), high-level editing (keyword-based image stylization), and image creation (sketch-driven iconification). First, by learning based on a massive online user study, we develop a metric for automatically predicting the perceptual quality of images produced by motion deblurring algorithms, without access to the original images. Second, by leveraging online image search engines, we investigate an approach to photo filtering that only requires the user to provide one or more keywords. Third, we develop a system that synthesize a variety of pictograms by remixing portions of icons retrieved from a large online repository.
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