2026-09-19T04:57:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1584882025-04-07T08:59:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Durand, Frédo
Biscarrat, Camille
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-03-12T16:55:15Z
2025-03-12T16:55:15Z
2024-09
2025-03-04T18:44:24.022Z
https://hdl.handle.net/1721.1/158488
Burst imaging is a technique that consists of taking multiple images in quick succession and merging them into one output image. By aligning and combining data from multiple frames, we can increase resolution, attenuate noise, reduce motion blur and expand the dynamic range to obtain a higher quality image. In this thesis, we propose a method that learns continuous kernels to process and merge burst frames. We show that the learned kernels adapt to local image information and take advantage of sub-pixel sample location information to demosaic, denoise and merge the burst into a high quality output.
S.M.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Burst Imaging with Learned Continuous Kernels
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Burst imaging is a technique that consists of taking multiple images in quick succession and merging them into one output image. By aligning and combining data from multiple frames, we can increase resolution, attenuate noise, reduce motion blur and expand the dynamic range to obtain a higher quality image. In this thesis, we propose a method that learns continuous kernels to process and merge burst frames. We show that the learned kernels adapt to local image information and take advantage of sub-pixel sample location information to demosaic, denoise and merge the burst into a high quality output.</Abstract>
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