Super Resolution results in PANOPTES, an adaptive multi-aperture folded architecture

Abstract

We present experimental results of digital super resolution (DSR) techniques on low resolution data collected using PANOPTES, a multi-aperture miniature folded imaging architecture. The flat form factor of PANOPTES architecture results in an optical system that is heavily blurred with space variant PSF which makes super resolution challenging. We also introduce a new DSR method called SRUM (Super-Resolution with Unsharpenning Mask) which can efficiently highlight edges by embedding an unsharpenning mask to the cost function. This has much better effect than just applying the mask after all iterations as a post-processing step.

Super-resolution: a comprehensive survey

“…In other words, multiple-image based SR is possible if at least one of the parameters involved in the imaging model employed changes from one LR image to another. These parameters include motion, blur (optical, atmospheric, and/or motion blur), zoom, multiple aperture [106], [446], multiple images from different sensors [117], [205], and different channels of a color image [117]. Therefore, in multiple-image SR prior to the actual reconstruction, a registration step is required to compensate for such changes, though, some of the methods (discussed in Section 6.1.2) do the reconstruction and the compensation of the changes simultaneously.…”

“…The main problem with the above mentioned IBP methods is that the response of the iteration can either converge to one of the possible solutions or it may oscillate between them [8], [13], [14], [20], [75], [83]. However, this can be dealt with by incorporating a priori knowledge about the solution, as has been done in [81], [83], [124], [279], [280], [380], [406], [446], [492], [552]. In this case, these algorithms will try to minimize ||Af − g|| 2 + λ||ρ(f )|| 2 , wherein λ is a regularization coefficient and ρ is a constraint on the solution.…”

“…Beside increasing the speed of the algorithm it has been shown that this increases the robustness of the algorithm against the outliers which can be generated by different sources of errors, such as errors in the motion estimation [124]. Table 5 Reported IBP works [5], [6], [8], [13], [14] [124], [125], [128], [138], [147], [172], [177], [242], [249], [264], [270], [279], [280] [284], [325], [369], [392], [406], [446], [492], [501], [539], [546], [552] …”

Prototype development and field-test results of an adaptive multiresolution PANOPTES imaging architecture

“…The DSR algorithm applied to the low-resolution (LR) images is known as superresolution with unsharp masking (SRUM), which we first introduced in [18]. This section briefly describes this DSR algorithm and presents the results of its application to the field-test data.…”

“…The linear forward imaging model in the spatial domain that illustrates the process of generating LR images from a HR image can be defined as [18,19] g k x ↓ ; y ↓ f w k x; y ⊗ h k x; y↓ L n k x ↓ ; y ↓ ; k 1; …; N;…”