title: Computational Long Exposure Mobile Photography
summary: Long exposure photography produces stunning imagery, representing moving
  elements in a scene with motion-blur. It is generally employed in two
  modalities, producing either a foreground or a background blur effect.
  Foreground blur images are traditionally captured on a tripod-mounted camera
  and portray blurred moving foreground elements, such as silky water or light
  trails, over a perfectly sharp background landscape. Background blur images,
  also called panning photography, are captured while the camera is tracking a
  moving subject, to produce an image of a sharp subject over a background
  blurred by relative motion. Both techniques are notoriously challenging and
  require additional equipment and advanced skills. In this paper, we describe a
  computational burst photography system that operates in a hand-held smartphone
  camera app, and achieves these effects fully automatically, at the tap of the
  shutter button. Our approach first detects and segments the salient subject. We
  track the scene motion over multiple frames and align the images in order to
  preserve desired sharpness and to produce aesthetically pleasing motion
  streaks. We capture an under-exposed burst and select the subset of input
  frames that will produce blur trails of controlled length, regardless of scene
  or camera motion velocity. We predict inter-frame motion and synthesize
  motion-blur to fill the temporal gaps between the input frames. Finally, we
  composite the blurred image with the sharp regular exposure to protect the
  sharpness of faces or areas of the scene that are barely moving, and produce a
  final high resolution and high dynamic range (HDR) photograph. Our system
  democratizes a capability previously reserved to professionals, and makes this
  creative style accessible to most casual photographers.
    More information and supplementary material can be found on our project
  webpage: https://motion-mode.github.io/
paper_authors._id: 64cc6807c7f30fbf7baf5b7c
paper_authors.name: Eric Tabellion
paper_authors._id: 64cc6807c7f30fbf7baf5b7d
paper_authors.name: Nikhil Karnad
paper_authors._id: 64cc6807c7f30fbf7baf5b7e
paper_authors.name: Noa Glaser
paper_authors._id: 64cc6807c7f30fbf7baf5b7f
paper_authors.name: Ben Weiss
paper_authors._id: 64cc6807c7f30fbf7baf5b80
paper_authors.name: David E. Jacobs
paper_authors._id: 64cc6807c7f30fbf7baf5b81
paper_authors.name: Yael Pritch
paper_title: Computational Long Exposure Mobile Photography
paper_summary: Long exposure photography produces stunning imagery, representing moving
  elements in a scene with motion-blur. It is generally employed in two
  modalities, producing either a foreground or a background blur effect.
  Foreground blur images are traditionally captured on a tripod-mounted camera
  and portray blurred moving foreground elements, such as silky water or light
  trails, over a perfectly sharp background landscape. Background blur images,
  also called panning photography, are captured while the camera is tracking a
  moving subject, to produce an image of a sharp subject over a background
  blurred by relative motion. Both techniques are notoriously challenging and
  require additional equipment and advanced skills. In this paper, we describe a
  computational burst photography system that operates in a hand-held smartphone
  camera app, and achieves these effects fully automatically, at the tap of the
  shutter button. Our approach first detects and segments the salient subject. We
  track the scene motion over multiple frames and align the images in order to
  preserve desired sharpness and to produce aesthetically pleasing motion
  streaks. We capture an under-exposed burst and select the subset of input
  frames that will produce blur trails of controlled length, regardless of scene
  or camera motion velocity. We predict inter-frame motion and synthesize
  motion-blur to fill the temporal gaps between the input frames. Finally, we
  composite the blurred image with the sharp regular exposure to protect the
  sharpness of faces or areas of the scene that are barely moving, and produce a
  final high resolution and high dynamic range (HDR) photograph. Our system
  democratizes a capability previously reserved to professionals, and makes this
  creative style accessible to most casual photographers.
    More information and supplementary material can be found on our project
  webpage: https://motion-mode.github.io/
paper_ai_summary: A smartphone app automatically captures and composites foreground or background blur effects using computational burst photography techniques.
paper_ai_keywords: computational burst photography salient subject detection scene motion tracking image alignment under-exposed burst inter-frame motion prediction motion-blur synthesis image compositing high dynamic range