Super resolution (SR) for real-life video sequences is a challenging problem due to complex nature of the motion fields. In this paper, a novel blind SR method is proposed to improve the spatial resolution of video sequences, while the overall point spread function of the imaging system, motion fields, and noise statistics are unknown. To estimate the blur(s), first, a nonuniform interpolation SR method is utilized to upsample the frames, and then, the blur(s) is(are) estimated through a multi-scale process. The blur estimation process is initially performed on a few emphasized edges and gradually on more edges as the iterations continue. Also for faster convergence, the blur is estimated in the filter domain rather than the pixel domain. The high-resolution frames are estimated using a cost function that has the fidelity and regularization terms of type Huber-Markov random field to preserve edges and fine details. The fidelity term is adaptively weighted at each iteration using a masking operation to suppress artifacts due to inaccurate motions. Very promising results are obtained for real-life videos containing detailed structures, complex motions, fast-moving objects, deformable regions, or severe brightness changes. The proposed method outperforms the state of the art in all performed experiments through both subjective and objective evaluations. The results are available online at http://lyle.smu.edu/~rajand/Video_SR/.

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.

The present work describes an active stereo apparatus that can not only recover scene geometry but also resolve spatial detail beyond the camera optical cutoff. The apparatus is comprised of a camera and a projector whose center-of-perspective is located in the camera pupil plane1. The scene is illuminated with warped sinusoidal patterns as opposed to periodic or coded patterns. The findings reported in this work can help design imaging systems that feature improved optical resolution and 3D acquisition capabilities.

We present experimental results of digital super resolution (DSR) techniques on low resolution data collected using PANOPTES, a multi-aperture miniature folded imaging architecture. The flat form factor of PANOPTES architecture results in an optical system that is heavily blurred with space variant PSF which makes super resolution challenging. We also introduce a new DSR method called SRUM (Super-Resolution with Unsharpenning Mask) which can efficiently highlight edges by embedding an unsharpenning mask to the cost function. This has much better effect than just applying the mask after all iterations as a post-processing step.

Using intelligent resource allocation based on the information content in the imaging system's field-of-view for the successful design of a flat-profile multiplexed optical imaging system requires the use of adaptive techniques. This paper describes a model-based technique for determining regions of interest in aerial images using the 2D normalized power spectral density within Gilles' saliency map estimator. The proposed technique exploits the 1/falphaspatial spectral shape of such natural imagery in a computationally-simple approach that is robust to additive noise. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data.

The importance of mathematics and science education in today's modern, technology-driven society cannot be understated. This paper describes the history of and ongoing efforts in curricular and educational technology development for the infinity project, a joint effort between educators, administrators, and industry leaders to establish an engineering curriculum at the high school level. Several issues are considered, including the choice and design of the technology platform used in the curriculum, the curricular and technology development timeline, and the design examples chosen to illuminate important engineering concepts within the curriculum. We also provide a list of key best practices and challenges in developing novel pre-college engineering curricula for future engineers.

Multiplexed image reconstruction, estimating high resolution images from multiple low resolution images with highly overlapped fields of view, is improved when the magnification of the imagers is diverse. No assumptions of shift invariance or Toeplitz structure are required for computational manageability because localized reconstruction is possible and sensitivity to boundary conditions is reduced. Such multiplexed diverse image sensors have applications in flat sensor systems for surveillance and pervasive personal imaging.

We propose a novel method for the optimal co-design of the optical and reconstruction filters in a computational imaging system. Closed form solutions are presented for the design of an optimal observation matrix for a fixed reconstruction matrix. An iterative method for computing global optimal filters is then proposed based on the derived analytical solution and the well known Wiener filter. The performance of the proposed optimal filters represents a universal bound on the performance of any physically realizable computational imaging system.

Reconstruction algorithms to compute a single improved resolution image from multiple lower resolution images have application in the design of cameras with flat form factors. The accuracy of these reconstructions depend on measurement noise, measurement quantization, the structure of the image acquisition system, and the accuracy of the image acquisition model. The paper compares the expected and simulated performance for reconstructions from multiple lower resolution images. The analysis shows that designs using lenses with different imaging characteristics significantly improve the theoretical performance results. In addition, lens diversity allows the reconstruction problem to be naturally partitioned into a set of loosely coupled smaller reconstructions that are computationally more manageable.

The design, packaging approach, and experimental evaluation of the free-space accelerator for switching terabit networks (FAST-Net) smart-pixel-based optical interconnection prototype are described. FAST-Net is a high-throughput data-switching concept that uses a reflective optical system to globally interconnect a multichip array of smart pixel devices. The three-dimensional optical system links each chip directly to every other with a dedicated bidirectional parallel data path. in the experiments, several prototype smart-pixel devices were packaged on a common multichip module (MCM) with interchip registration accuracies of 5-10 /spl mu/m. The smart-pixel arrays (SPAs) consist of clusters of oxide-confined vertical-cavity surface-emitting lasers and photodetectors that are solder bump-bonded to Si integrated circuits. The optoelectronic elements are arranged within each cluster on a checkerboard pattern with 125-/spl mu/m pitch. The experimental global optical interconnection module consists of a mirror and lens array that are precisely aligned to achieve the required interchip parallel connections between up to 16 SPAs. Five prototype SPAs were placed on the MCM to allow the evaluation of a variety of interchip links. Measurements verified the global link pattern across several devices on the MCM with high optical resolution and registration. No crosstalk between adjacent channels was observed after alignment. The I/O density and efficiency results suggest that a multi-terabit switch module that incorporates global optical interconnection to overcome conventional interconnection bottlenecks is feasible.