Velocity-Dependent Shutter Sequences for Motion Deblurring

Abstract

Abstract. We address the problem of high-quality image capture of fast-moving objects in moderate light environments. In such cases, the use of a traditional shutter is known to yield non-invertible motion blur due to the loss of certain spatial frequencies. We extend the flutter shutter method of Raskar et al. to fast-moving objects by first demonstrating that no coded exposure sequence yields an invertible point spread function for all velocities. Based on this, we argue that the shutter sequence must be dependent on object velocity, and propose a method for computing such velocity-dependent sequences. We demonstrate improved image quality from velocity-dependent sequences on fast-moving objects, as compared to sequences found using the existing sampling method.

An overview of computational photography

“…Another way is to encode the high frequency information of the moving objects or the relative motion between the camera and the scene in some sense, and perform better restoration. The various approaches have been proposed, including: interleaving a camera array in chronological order [84,125], using a hybrid array [126]; coded photography (e.g., coded exposure [31,[127][128][129][130][131], coded sampling [132,133,21](see Figure 5(b)), coded sensor motion [134,135]); temporal super-resolution, which estimates the point spread function (blur kernel) from an image sequence [136][137][138] or a single photograph [139] before deconvolution, or performs temporal super-resolution from a set of low rate videos [140,141], or even introduces hardware attachments [142].…”

Iris capture from moving subjects using a fluttering shutter

“…For the flutter shutter images, the blur PSF B is a flutter shutter PSF generated using the method of [9], with equivalent exposures to the 8, 16, 24, and 32 pixel rectangle PSFs, and images are de-blurred using the de-convolution code provided in [12]. At a resolution of 8 pixels per mm on the iris, and for an exposure time of 2ms, these correspond to velocities of 0.5, 1, 1.5 and 2 meters per second, respectively.…”

Efficient non-uniform deblurring based on generalized additive convolution model

“…Blind deblurring from a real-world blurry image that needs to estimate the blur procedure and latent sharp image is a very ill-posed problem. Recently, several hardware-assisted approaches had been developed [1][2][3][4][5][6], by which additional information can be acquired to reduce the ill-posedness of the blind deblurring. These hardware-assisted approaches can provide much easier deviation of the blur or sharp image, but require complex camera configurations [1,2] or dedicatedly designed hardware support [3][4][5][6], far from taking the place of traditional imaging devices.…”

“…Recently, several hardware-assisted approaches had been developed [1][2][3][4][5][6], by which additional information can be acquired to reduce the ill-posedness of the blind deblurring. These hardware-assisted approaches can provide much easier deviation of the blur or sharp image, but require complex camera configurations [1,2] or dedicatedly designed hardware support [3][4][5][6], far from taking the place of traditional imaging devices. At the same time, in the era of ubiquitous acquisition of digital images using portable imaging devices, e.g., digital camera, mobile phone, camera shake is often unavoidable during exposure procedure, a major cause that ruins *Correspondence: cswmzuo@gmail.com School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, 150001 Harbin, China a photograph.…”