Super-resolution based on low-resolution, warped images

摘要

Super-resolution based on sequences of low-resolution images has many applications. Among these is improving the image quality of video images, particularly images of historical interest and images from security cameras. Successive frames have slightly different views, or projections, of the object. Not unlike the methods used in computerized tomography, these projections can be combined to produce an image with better resolution than any of the low-resolution views. We observe that in real images even the simplest objects are warped in successive frames. We estimate the warping parameters of each frame and then estimate the object by iterative deconvolution. This forces an appropriate match between a model for the data and the actual data. We show computer simulations of the method and we show some experimental results.