Super-resolution Using Constrained Deep Texture Synthesis

Hallucinating high frequency image details in single image super-resolution is a challenging task. Traditional super-resolution methods tend to produce oversmoothed output images due to the ambiguity in mapping between low and high resolution patches. We build on recent success in deep learning based texture synthesis and show that this rich feature space can facilitate successful transfer and synthesis of high frequency image details to improve the visual quality of super-resolution results on a wide variety of natural textures and images.

Lens Factory: Automatic Lens Generation Using Off-the-shelf Components

Custom optics is a necessity for many imaging applications. Unfortunately, custom lens design is costly (thousands to tens of thousands of dollars), time consuming (10-12 weeks typical lead time), and requires specialized optics design expertise. By using only inexpensive, off-the-shelf lens components the Lens Factory automatic design system greatly reduces cost and time. Design, ordering of parts, delivery, and assembly can be completed in a few days, at a cost in the low hundreds of dollars. Lens design constraints, such as focal length and field of view, are specified in terms familiar to the graphics community so no optics expertise is necessary. Unlike conventional lens design systems, which only use continuous optimization methods, Lens Factory adds a discrete optimization stage. This stage searches the combinatorial space of possible combinations of lens elements to find novel designs, evolving simple canonical lens designs into more complex, better designs. Intelligent pruning rules make the combinatorial search feasible. We have designed and built several high performance optical systems which demonstrate the practicality of the system.