Synthetic Shutter Speed Imaging

Telleen, J.; Sullivan, Anne; Yee, Jerry; Wang, Oliver; Gunawardane, Prabath; Collins, I.R.; Davis, James

doi:10.1111/j.1467-8659.2007.01082.x

Cited by 66 publications

(42 citation statements)

References 16 publications

(12 reference statements)

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“…This property makes nonlocal means excellent for denoising from a burst of unaligned shots, which may contain objects that deform or change their appearance. Existing methods for denoising from multiple shots or videos either globally align and average (such as the work of [Telleen et al 2007] and [Adams et al 2008]) or search for explicit block matches (such as [Avanaki 2006]). These methods are fairly brittle.…”

Section: Resultsmentioning

confidence: 99%

Gaussian KD-trees for fast high-dimensional filtering

et al. 2009

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Figure 1: The Gaussian kd-tree accelerates a broad class of non-linear filters, including the bilateral (left), non-local means (middle), and a novel non-local means for geometry (right). AbstractWe propose a method for accelerating a broad class of non-linear filters that includes the bilateral, non-local means, and other related filters. These filters can all be expressed in a similar way: First, assign each value to be filtered a position in some vector space. Then, replace every value with a weighted linear combination of all values, with weights determined by a Gaussian function of distance between the positions. If the values are pixel colors and the positions are (x, y) coordinates, this describes a Gaussian blur. If the positions are instead (x, y, r, g, b) coordinates in a five-dimensional space-color volume, this describes a bilateral filter. If we instead set the positions to local patches of color around the associated pixel, this describes non-local means. We describe a Monte-Carlo kdtree sampling algorithm that efficiently computes any filter that can be expressed in this way, along with a GPU implementation of this technique. We use this algorithm to implement an accelerated bilateral filter that respects full 3D color distance; accelerated non-local means on single images, volumes, and unaligned bursts of images for denoising; and a fast adaptation of non-local means to geometry. If we have n values to filter, and each is assigned a position in a d-dimensional space, then our space complexity is O(dn) and our time complexity is O(dn log n), whereas existing methods are typically either exponential in d or quadratic in n.

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Section: Resultsmentioning

confidence: 99%

Gaussian KD-trees for fast high-dimensional filtering

et al. 2009

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“…In contrast, our work modifies these parameters in a postprocess. Our contributions can be seen as a generalization and extension of synthetic shutter speed [6], which imitates a long exposure shot by taking a series of short exposure photographs and aligning them, reducing noise and camera shake while preserving motion blur. Our filtering also differs from traditional temporal downsampling of HFR videos [7] by taking the viewer into account.…”

Section: Related Workmentioning

confidence: 99%

Temporal Video Filtering and Exposure Control for Perceptual Motion Blur

Stengel

Bauszat

Eisemann

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose the computation of a perceptual motion blur in videos. Our technique takes the predicted eye motion into account when watching the video. Compared to traditional motion blur recorded by a video camera our approach results in a perceptual blur that is closer to reality. This postprocess can also be used to simulate different shutter effects or for other artistic purposes. It handles real and artificial video input, is easy to compute and has a low additional cost for rendered content. We illustrate its advantages in a user study using eye tracking.Index Terms-high frame rate, temporal filtering, perception, sharpening and blur ✦

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“…Optical flow, however, is difficult to compute for complex motion. Several recent works exploiting temporal information for image enhancement assume simplified transformation between frames, such as translation in [5] and homography in [10,23]. Handling more complex motion requires user assistance as in [5].…”

Section: Related Workmentioning

confidence: 99%

Denoising vs. deblurring: HDR imaging techniques using moving cameras

Zhang

Deshpande

Chen³

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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New cameras such as the Canon EOS 7D and Pointgrey Grasshopper have 14-bit sensors. We present a theoretical analysis and a practical approach that exploit these new cameras with high-resolution quantization for reliable HDR imaging from a moving camera. Specifically, we propose a unified probabilistic formulation that allows us to analytically compare two HDR imaging alternatives: (1) deblurring a single blurry but clean image and (2) denoising a sequence of sharp but noisy images. By analyzing the uncertainty in the estimation of the HDR image, we conclude that multi-image denoising offers a more reliable solution. Our theoretical analysis assumes translational motion and spatially-invariant blur. For practice, we propose an approach that combines optical flow and image denoising algorithms for HDR imaging, which enables capturing sharp HDR images using handheld cameras for complex scenes with large depth variation. Quantitative evaluation on both synthetic and real images is presented.

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Synthetic Shutter Speed Imaging

Cited by 66 publications

References 16 publications

Gaussian KD-trees for fast high-dimensional filtering

Gaussian KD-trees for fast high-dimensional filtering

Temporal Video Filtering and Exposure Control for Perceptual Motion Blur

Denoising vs. deblurring: HDR imaging techniques using moving cameras

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