Jittered Exposures for Image Super-Resolution Nianyi Li1 Scott McCloskey2 Jingyi Yu3,1 1University of Delaware, Newark, DE, USA.
[email protected] 2Honeywell ACST, Golden Valley, MN, USA.
[email protected] 3ShanghaiTech University, Shanghai, China.
[email protected] Abstract The process can be formulated using an observation mod- el that takes low-resolution (LR) images L as the blurred Camera design involves tradeoffs between spatial and result of a high resolution image H: temporal resolution. For instance, traditional cameras pro- vide either high spatial resolution (e.g., DSLRs) or high L = W H + n (1) frame rate, but not both. Our approach exploits the optical stabilization hardware already present in commercial cam- where W = DBM, in which M is the warp matrix (transla- eras and increasingly available in smartphones. Whereas tion, rotation, etc.), B is the blur matrix, D is the decimation single image super-resolution (SR) methods can produce matrix and n is the noise. convincing-looking images and have recently been shown Most state-of-the-art image SR methods consist of three to improve the performance of certain vision tasks, they stages, i.e., registration, interpolation and restoration (an in- are still limited in their ability to fully recover informa- verse procedure). These steps can be implemented separate- tion lost due to under-sampling. In this paper, we present ly or simultaneously according to the reconstruction meth- a new imaging technique that efficiently trades temporal ods adopted. Registration refers to the procedure of estimat- resolution for spatial resolution in excess of the sensor’s ing the motion information or the warp function M between pixel count without attenuating light or adding additional H and L.