The recovery of sparse images from noisy, blurry, and potentially low-dimensional observations can be accomplished by solving an optimization problem that minimizes the least-squares error in data fidelity with a sparsity-promoting regularization term (the so-called ℓ2- ℓ1minimization problem). This paper focuses on the reconstruction of a video sequence of images where known pixel-intensity bounds exist at each video frame. It has been established that the ℓ2- ℓ1minimization problem can be solved efficiently using gradient projection, which was recently extended to solve general bound-constrained ℓ2- ℓ1minimization problems. Furthermore, the video reconstruction can be made more efficient by exploiting similarities between consecutive frames. In this paper, we propose a method for reconstructing a video sequence that takes advantage of the inter-frame correlations while constraining the solution to satisfy known a priori bounds, offering a higher potential for increasingly accurate reconstructions. To demonstrate the effectiveness of this approach, we have included the results of our numerical experiments.

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As a corporation, our top priority is customer satisfaction. Worldwide customer satisfaction requires that we engineer, manufacture, and market the highest quality information systems in the industry. It also requires that we provide prompt efficient service in the field to these systems. Field Service is a strategic competitive asset that contributes almost a third of the corporation's revenues. Field Service Logistics supports, worldwide, both our Field Service engineers and our original equipment manufacturers (OEM's) with necessary preventive and remedial spare parts. These parts ensure that our customer's investments in our hardware and software remain as accessible as possible. This paper considers the logistics and repair opportunities in the computer and electronics industry, both present and future, based on our knowledge of how technology is changing. The concept of fieldreplaceable units (FRU's) having a planned quality life cycle is introduced. Thus, customer needs can be translated into a set of systems designed to ensure that the preventive and remedial materials provided are fit for use.