Improving resolution to reduce aliasing in an undersampled image sequance

Improving resolution to reduce aliasing in an undersampled image sequance

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C.L. Luengo Hendriks SPIE vol. 3965, January 2000 1 Improving resolution to reduce aliasing in an undersampled image sequence C.L. Luengo Hendriks * , L.J. van Vliet Pattern Recognition Group, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands ABSTRACT We present various methods to increase the spatial resolution of an undersampled, and thus aliased, image sequence. The sequence is acquired by an infrared camera, which severely undersamples the image of a static scene. Vibration of the setup causes a random, sub-pixel, global translation of the scene before sampling. Although a single frame is hampered by aliasing, a sufficiently large series of such frames contains all the information needed to undo this aliasing. The global translation among the frames is estimated based only on the image content. These shift estimates enable us to fuse the frames in the sequence, providing a high-resolution image, which is done using interpolation schemes applicable to non-uniformly sampled data. These interpolation schemes have a built-in regularization to balance resolution enhancement and increase of signal-to-noise ratio. The proposed methods are of moderate computational complexity and can be implemented in hardware for real-time use. They prove to be robust under noisy circumstances, and the performance degrades “gracefully” when the number of input frames decreases. We compare our results with alternative methods involving iterative algorithms, which are not practical for real-time application, and a spectral estimation algorithm, which is too sensitive to noise and errors in the shift estimation. Keywords: Sub-pixel image registration, interpolation, random sampling, aliasing, superresolution. 1. INTRODUCTION The lens system in a camera limits the bandwidth of the image projected onto the detector array. The detector array can then sample this image. If the sampling satisfies the Nyquist criterion, the image projected onto the detector can be exactly reconstructed from the samples. Detector elements need to be large to collect light efficiently. In infrared imaging, these elements also need to be isolated from each other, which makes the fill factor small and the pixel pitch large. This causes the array not to be sufficiently dense to sample according to Nyquist, and the sampled images are aliased. Some IR camera manufacturers solve this undersampling problem by mounting the detector array on a piezo-electric element that allows scanning of the image projected onto the detector plane (‘microscanning’). The previous discussion is also valid for other types of camera; for example, Carl-Zeiss incorporates this technique is the ProgRes digital camera. The approach explored in this paper is to induce a random motion by vibration of the camera, and combine a series of frames to create a single image with a higher spatial resolution. Note that this can only work if there is a sub-pixel translation between those frames. This translation causes the image to be sampled at more points than provided by the
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