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Depth estimation, spatially variant image registration, and super-resolution using a multi-lenslet camera

With a multi-lenslet camera, we can capture multiple low resolution (LR) images of the same scene and use them to reconstruct a high resolution (HR) image. For this purpose, two major computation problems need to be solved, the image registration and the super resolution (SR) reconstruction. For the first, one major hurdle is the spatially variant shifts estimation, because objects in a scene are often at different depths, and due to parallax, shifts between imaged objects often vary on a pixel basis. This poses a great computational challenge as the problem is NP complete. The multi-lenslet camera with a single focal plane provides us a unique opportunity to take advantage of the parallax phenomenon, and to directly relate object depths with their shifts, and thus we essentially reduced the parameter space from a two dimensional (x, y) space to a one dimensional depth space, which would greatly reduce the computational cost. As results, not only we have registered LR images, the estimated depth map can also be valuable for some applications. After registration, LR images along with estimated shifts can be used to reconstruct an HR image. A previously developed algorithm will be employed to efficiently compute for a large HR image in the size of 1024x1024.