Optimal single image capture for motion deblurring

Abstract

Deblurring images of moving objects captured from a traditional camera is an ill-posed problem due to the loss of high spatial frequencies in the captured images. Techniques have attempted to engineer the motion point spread function (PSF) by either making it invertible using coded exposure, or invariant to motion by moving the camera in a specific fashion. We address the problem of optimal single image capture strategy for best deblurring performance. We formulate the problem of optimal capture as maximizing the signal to noise ratio (SNR) of the deconvolved image given a scene light level. As the exposure time increases, the sensor integrates more light, thereby increasing the SNR of the captured signal. However, for moving objects, larger exposure time also results in more blur and hence more deconvolution noise. We compare the following three single image capture strategies: (a) traditional camera, (b) coded exposure camera, and (c) motion invariant photography, as well as the best exposure time for capture by analyzing the rate of increase of deconvolution noise with exposure time. We analyze which strategy is optimal for known/unknown motion direction and speed and investigate how the performance degrades for other cases. We present real experimental results by simulating the above capture strategies using a high speed video camera.