&lt;title&gt;Superresolution of SAR images using Bayesian and convex set-theoretic approaches&lt;/title&gt;

Bhattacharjee, Supratik; Sundareshan, Malur K.

doi:10.1117/12.396330

Cited by 3 publications

(1 citation statement)

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“…The basic idea of super-resolution is to 2011LCG capture a sequence of slightly shifted images and fuse them together to generate a higher resolution image. With the availability of more computational power and speed, this technique has been applied to complement hardware in various imaging modalities such as digital cameras [5][6][7][8][9][10], synthetic aperture radar [11,12], ultrasound [13], and MRI [14] to name a few. Many SR algorithms have been proposed in the literature with the common goal of estimating high resolution images as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution imaging combining the design of an optical coherence microscope objective with liquid-lens based dynamic focusing capability and computational methods

et al. 2008

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In this paper, we present the design of a 0.2 NA microscope objective operating across a 120nm broadband spectral range that requires only two doublets and an embedded liquid lens to achieve 3µm invariant lateral resolution throughout a large 8 cubic millimeter imaging sample. Achieving invariant lateral resolution comes with some sacrifice in imaging speed, yet in the approach proposed, high speed in vivo imaging is maintained up to a resolution of 3 µm for a 2x2 mm sample size. Thus, in anticipation to ultimately aim for a resolution of 0.5 to 1 µm, we are investigating the possibility to further gain in resolution using super-resolution methods so both hardware solutions and image processing methods together can provide the best trade-off in overall resolution and speed of imaging. As a starting point to investigate super-resolution methods, we evaluate in this paper three well-known super-resolution algorithms used to reconstruct a high resolution image from down-sampled low resolution images of an African frog tadpole acquired en face using our OCM set-up. To establish ground truth necessary for assessment of the methods, low resolution images were simulated from a high resolution OCM image. The specification and design performance of the custom designed microscope will be presented as well as our first results of super-resolution imaging. The performance of each algorithm was analyzed and all performances compared using two different metrics. Early results indicate that super-resolution may play a significant role in the optimization of high invariant resolution OCM systems.

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