UC San Diego

The digital imaging revolution has made the camera ubiquitous; however, image quality has not improved at the same rate as the increase in camera availability. Increasingly more cameras are small, with inexpensive lenses, no flash, and lightweight bodies that are difficult to hold steady, and this results in images with blur, noise, and poor color-balance. Consequently, there is a strong need for simple, automatic, and accurate methods for image correction. This dissertation, presents work that uses "content-specific" image models and priors for image enhancement. Image enhancement is a challenge problem -- corrections such as deblurring, denoising, and color-correction are ill-posed, where the number of unknown values outweighs the number of observations. As a result, it is necessary to add additional information as constraints. Previous work has focused on using generic image priors that are applicable to a large number of images. In this work, we develop constraints that are tuned to the specific content of an image. First, we discuss a fast, accurate blur estimation method that models all edges in a sharp image as step-edges. The method predicts the "sharp" version of a blurry input image and uses the two images together to solve for a PSF. Second, we discuss a framework for image deblurring and denoising that uses local color statistics to produce sharp, low-noise results. Even when the blur function is known, deblurring an image is still quite difficult due to information loss during blurring and due to the presence of noise. In our work, we investigate using local-color statistics of an image in a joint framework for deblurring and denoising of images. Lastly, we discuss work in methods that use "identity-specific" priors to perform corrections for images containing faces. These priors provide the guidance needed to perform high-quality corrections needed for known, familiar faces. Deblurring, super-resolution, color-balancing, and exposure correction operate independently, so that a user can correct selected image properties, while still retaining certain desired qualities of the original photo. We have also developed a prototype application for performing these corrections