Computational Long Exposure Mobile Photography

• URL: http://arxiv.org/abs/2308.01379v1
• Date: Wed, 2 Aug 2023 18:36:54 GMT
• Title: Computational Long Exposure Mobile Photography
• Authors: Eric Tabellion, Nikhil Karnad, Noa Glaser, Ben Weiss, David E. Jacobs, Yael Pritch
• Abstract summary: We describe a computational burst photography system that operates in a hand-held smartphone camera app. Our approach first detects and segments the salient subject. 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.
• Score: 1.5553309483771411
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

Related papers

• Motion Blur Decomposition with Cross-shutter Guidance [33.72961622720793]
  Motion blur is an artifact under insufficient illumination where exposure time has to be prolonged so as to collect more photons for a bright enough image. Recent researches have aimed at decomposing a blurry image into multiple sharp images with spatial and temporal coherence. We propose to utilize the ordered scanline-wise delay in a rolling shutter image to robustify motion decomposition of a single blurry image.
  arXiv  Detail & Related papers  (2024-04-01T13:55:40Z)
• ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images [58.24910105459957]
  We present ExBluRF, a novel view synthesis method for extreme motion blurred images. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes with the order of 10 times less training time and GPU memory consumption.
  arXiv  Detail & Related papers  (2023-09-16T11:17:25Z)
• 3D Moments from Near-Duplicate Photos [67.15199743223332]
  3D Moments is a new computational photography effect. We produce a video that smoothly interpolates the scene motion from the first photo to the second. Our system produces photorealistic space-time videos with motion parallax and scene dynamics.
  arXiv  Detail & Related papers  (2022-05-12T17:56:18Z)
• Learning Spatially Varying Pixel Exposures for Motion Deblurring [49.07867902677453]
  We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring. Our work illustrates the promising role that focal-plane sensor--processors can play in the future of computational imaging.
  arXiv  Detail & Related papers  (2022-04-14T23:41:49Z)
• Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels [48.063176079878055]
  One of the primary effects applied to images captured in portrait mode is a synthetic shallow depth of field (DoF) In this work, we follow the trend of rendering the NIMAT effect by introducing a modification on the blur synthesis procedure in portrait mode. Our modification enables a high-quality synthesis of multi-view bokeh from a single image by applying rotated blurring kernels.
  arXiv  Detail & Related papers  (2021-11-15T15:23:55Z)
• A Method For Adding Motion-Blur on Arbitrary Objects By using Auto-Segmentation and Color Compensation Techniques [6.982738885923204]
  In this paper, an unified framework to add motion blur on per-object basis is proposed. In the method, multiple frames are captured without motion blur and they are accumulated to create motion blur on target objects.
  arXiv  Detail & Related papers  (2021-09-22T05:52:27Z)
• Exposure Trajectory Recovery from Motion Blur [90.75092808213371]
  Motion blur in dynamic scenes is an important yet challenging research topic. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image.
  arXiv  Detail & Related papers  (2020-10-06T05:23:33Z)
• Rendering Natural Camera Bokeh Effect with Deep Learning [95.86933125733673]
  Bokeh is an important artistic effect used to highlight the main object of interest on the photo. Mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. We propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras.
  arXiv  Detail & Related papers  (2020-06-10T07:28:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.