You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method’s rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently. Experiments with real camera data, and extensive comparisons, show that the proposed Fourier burst accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. Finally, we also present experiments in real high dynamic range (HDR) scenes, showing how the method can be straightforwardly extended to HDR photography.

Cited Abstract(s):
[1]: We propose a new algorithm dramatically enhancing the efficiency of the lucky imaging technique for AO-corrected images in the visible range. It is achieved by a selection based on the relative strength of signal for each spatial frequency in the Fourier domain, making a more efficient use of information contained in each frame. Realistic simulations show that our algorithm allows us to reach the diffraction limit in the visible range on an AO-equipped 8 m telescope and enhances the Strehl ratio of an AO long exposure by a factor of up to 4. It outperforms the lucky imaging technique at an equivalent selection ratio. The fraction of selected data in simulation is also boosted from two to eight times for a given Strehl-ratio performance.
[1] introduced a selection scheme for astronomic images, based on the relative strength of signal for each spatial frequency in the Fourier domain. From a series of realistic image simulations, the authors showed that this procedure produces images of higher resolution and better signal to noise ratio than traditional lucky image fusion schemes. This procedure makes a much more efficient use of the information contained in each frame.