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

Creating realistic renderings in computer graphics depends on having knowledge of the reflectance properties of materials in the scene. For opaque materials the bi-directional reflectance function (BRDF) captures these properties. Full BRDFs can be measured using highly-accurate gonioreflectometers or more recently with techniques using digital cameras. These approaches sample the BRDF either densely and use simple interpolation for rendering with the BRDF or acquire data sparsely and fit to a BRDF. Previous methods require images from a number of camera positions with a variety of known light positions. We present a technique to recover appearance model parameters from a single image under arbitrary lighting. Using a single high dynamic range image of material sample of known geometry, an environment map, and an iterative rendering and optimization approach we can recover parameters for a wide variety of materials in short amount of time with less stringent experimental constraints as compared to previous techniques. Our technique in not limited to recovering BRDF parameters for opaque materials. We can fit parameters for any low parameter appearance model that is used in a typical renderer. We have measured a number of opaque and transparent materials and we present results showing comparisons of an image of a sample material and the rendered counterpart using the recovered parameters. We further use our optimization approach to recover BRDF model parameters from a single render image of a number of materials rendered from a data-driven BRDF model.

Pre-2018 CSE ID: CS2006-0868

We present a color calibration pipeline for large camera arrays. We assume static lighting conditions for each camera, such as studio lighting or a stationary array outdoors. We also assume we can place a planar calibration target so it is visible from every camera. Our goal is uniform camera color responses, not absolute color accuracy, so we match the cameras to each other instead of to a color standard. We first iteratively adjust the color channel gains and offsets for each camera to make their responses as similar as possible. This step white balances the cameras, and for studio applications, ensures that the range of intensities in the scene are mapped to the usable output range of the cameras. Residual errors are then calibrated in post-processing. We present results calibrating an array of 100 CMOS image sensors in different physical configurations, including closely or widely spaced cameras with overlapping fields of views, and tightly packed cameras with non-overlapping fields of view. The process is entirely automatic, and the camera configuration runs in less than five minutes on the 100 camera array.

Pre-2018 CSE ID: CS2005-0821

Recent far-reaching advances in synthetic biology have yielded exciting tools for the creation of new materials. Conversely, advances in the fundamental understanding of soft-condensed matter, polymers and biomaterials offer new avenues to extend the reach of synthetic biology. The broad and exciting range of possible applications have substantial implications to address grand challenges in health, biotechnology and sustainability. Despite the potentially transformative impact that lies at the interface of synthetic biology and biomaterials, the two fields have, so far, progressed mostly separately. This Perspective provides a review of recent key advances in these two fields, and a roadmap for collaboration at the interface between the two communities. We highlight the near-term applications of this interface to the development of hierarchically structured biomaterials, from bioinspired building blocks to 'living' materials that sense and respond based on the reciprocal interactions between materials and embedded cells.