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Camera Depth of Field Manipulation for Pre- and Post-Image Capture

Time: Wednesday 30-Sept-2020 23:30
Live in 11 days & 08:27:47


Artifacts

Motivation / Abstract
Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. Correcting defocus blur is challenging because the blur is spatially varying and difficult to estimate.  We  review an  effective  defocus  deblurring  method  that  exploits data available on dual-pixel (DP) sensors found on most modern cameras.  DP  sensors  are  used  to  assist  a  camera’s  auto-focus  by  capturing two sub-aperture views of the scene in a single image shot. The two sub-aperture images are used to calculate the appropriate lens position to focus on a particular scene region and are discarded afterwards. We introduce a deep neural network (DNN) architecture that uses these discarded sub-aperture images to reduce defocus blur. A key contribution  of  our  effort  is  a  carefully  captured  dataset  of  500  scenes  (2000 images) where each scene has: (i) an image with defocus blur captured at a large aperture; (ii) the two associated DP sub-aperture views; and (iii)  the  corresponding  all-in-focus  image  captured  with  a  small  aperture. Our proposed DNN produces results that are significantly better than  conventional  single  image  methods  in  terms  of  both  quantitative and perceptual metrics – all from data that is already available on the camera but ignored.
Questions Discussed
- Introducing the defocus blur problem in digital photography
- Pre-image capture focus adjustment Methods
- Post-image capture defocus deblurring
- Harnessing raw data from dual-pixel (DP) sensors 
- Deep Learning approach to tackle defocus deblurring
Stream Categories:
 Computer Vision

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