Learning to Extract a Video Sequence from a Single Motion-Blurred Image

We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulated over time during the exposure of the sensor. Unfortunately, reversing this process is nontrivial. Firstly, averaging destroys the temporal ordering of the frames. Secondly, the recovery of a single frame is a blind deconvolution task, which is highly ill-posed. We present a deep learning scheme that gradually reconstructs a temporal ordering by sequentially extracting pairs of frames. Our main contribution is to introduce loss functions invariant to the temporal order. This lets a neural network choose during training what frame to output among the possible combinations. We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. Our proposed method can successfully retrieve sharp image sequences from a single motion blurred image and can generalize well on synthetic and real datasets captured with different cameras

Blind Detection and Compensation of Camera Lens Geometric Distortions

This paper presents a blind detection and compensation technique for camera lens geometric distortions. The lens distortion introduces higher-order correlations in the frequency domain and in turn it can be detected using higher-order spectral analysis tools without assuming any specific calibration target. The existing blind lens distortion removal method only considered a single-coefficient radial distortion model. In this paper, two coefficients are considered to model approximately the geometric distortion. All the models considered have analytical closed-form inverse formulae.Comment: 6 pages, 4 figures, 2 table

Joint semi-blind detection and channel estimation in space-frequency trellis coded MIMO-OFDM

This paper considers an OFDM system with a multiple-input multiple-output (MIMO) configuration, which uses space-frequency trellis coding (SFTC). A novel method of decoding SFTC without a need to transmit separate training sequences is developed. The technique uses only a single frequency tone to acquire a complete set of the channels' estimates while performing SFTC detection. The method is akin to blind trellis search techniques (per-survivor processing - PSP) and adaptive Viterbi. Our solution consists of the deployment of a bank of Kalman filters. The bank of Kalman filters is coupled with Viterbi type decoders, which produce tentative decisions based on Kalman channel predictions. In return, the Kalman filters use the tentative decisions to update and track the MIMO channels corresponding to a number of tracked hypothese

Assessing Inconspicuous Smartphone Authentication for Blind People

As people store more personal data in their smartphones, the consequences of having it stolen or lost become an increasing concern. A typical counter-measure to avoid this risk is to set up a secret code that has to be entered to unlock the device after a period of inactivity. However, for blind users, PINs and passwords are inadequate, since entry 1) consumes a non-trivial amount of time, e.g. using screen readers, 2) is susceptible to observation, where nearby people can see or hear the secret code, and 3) might collide with social norms, e.g. disrupting personal interactions. Tap-based authentication methods have been presented and allow unlocking to be performed in a short time and support naturally occurring inconspicuous behavior (e.g. concealing the device inside a jacket) by being usable with a single hand. This paper presents a study with blind users (N = 16) where an authentication method based on tap phrases is evaluated. Results showed the method to be usable and to support the desired inconspicuity.Comment: 4 pages, 1 figur