Subpixel point spread function estimation from two photographs at different distances

In most digital cameras, and even in high-end digital single lens reflex cameras, the acquired images are sampled at rates below the Nyquist critical rate, causing aliasing effects. This work introduces an algorithm for the subpixel estimation of the point spread function (PSF) of a digital camera from aliased photographs. The numerical procedure simply uses two fronto-parallel photographs of any planar textured scene at different distances. The mathematical theory developed herein proves that the camera PSF can be derived from these two images, under reasonable conditions. Mathematical proofs supplemented by experimental evidence show the well-posedness of the problem and the convergence of the proposed algorithm to the camera in-focus PSF. An experimental comparison of the resulting PSF estimates shows that the proposed algorithm reaches the accuracy levels of the best nonblind state-of-the-art methods

Image enhancement methods and applications in computational photography

Computational photography is currently a rapidly developing and cutting-edge topic in applied optics, image sensors and image processing fields to go beyond the limitations of traditional photography. The innovations of computational photography allow the photographer not only merely to take an image, but also, more importantly, to perform computations on the captured image data. Good examples of these innovations include high dynamic range imaging, focus stacking, super-resolution, motion deblurring and so on. Although extensive work has been done to explore image enhancement techniques in each subfield of computational photography, attention has seldom been given to study of the image enhancement technique of simultaneously extending depth of field and dynamic range of a scene. In my dissertation, I present an algorithm which combines focus stacking and high dynamic range (HDR) imaging in order to produce an image with both extended depth of field (DOF) and dynamic range than any of the input images. In this dissertation, I also investigate super-resolution image restoration from multiple images, which are possibly degraded by large motion blur. The proposed algorithm combines the super-resolution problem and blind image deblurring problem in a unified framework. The blur kernel for each input image is separately estimated. I also do not make any restrictions on the motion fields among images; that is, I estimate dense motion field without simplifications such as parametric motion. While the proposed super-resolution method uses multiple images to enhance spatial resolution from multiple regular images, single image super-resolution is related to techniques of denoising or removing blur from one single captured image. In my dissertation, space-varying point spread function (PSF) estimation and image deblurring for single image is also investigated. Regarding the PSF estimation, I do not make any restrictions on the type of blur or how the blur varies spatially. Once the space-varying PSF is estimated, space-varying image deblurring is performed, which produces good results even for regions where it is not clear what the correct PSF is at first. I also bring image enhancement applications to both personal computer (PC) and Android platform as computational photography applications

Enhancing photographs using content-specific image priors

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 correction