Distributed and binary imaging using an array of low-resolution cameras
William Hasenplaugh, Mark Allen Neifeld · Frontiers in Optics · 2003
A variant of the general super-resolution problem involves the generation of a single high-resolution image using a sequence of low-resolution video frames [1]. We study a related problem in which a high-resolution image is obtained from multiple low-resolution cameras. In our model each camera measurement represents a transformation of the object space according to some (a priori known) position/magnification/rotation. Note that an array of imagers can be characterized by their number as well as their range of positions/magnifications/rotations. We refer to the range of position/magnification/rotation as the measurement diversity of the array. Large diversity might correspond to a range of positions = 16 pixels, a range of rotations = 45 degrees, and a range of magnifications = 20 %. Medium diversity might be considered 8 pixels, 15 degrees, and 10%. Random noise is also included within our imager model. We have developed several algorithms for combining data from disparate camera measurements in order to arrive at a single high-resolution object estimate. The first algorithm that we investigated was a variant of the gradient-based Iterative Back-Projection (IBP) algorithm [2], extended to handle general affine transformations. The results of applying this algorithm are shown in figure 1, in which we see that increasing the number of cameras improves reconstruction mean-squared-error (MSE). This figure also demonstrates the important trend that (for a sufficient number of cameras) increased measurement diversity provides improved MSE performance.