Consumer-level cameras are affordable for customers. While handy and easy to use, images and videos are likely to suffer from motion blur effect, especially under low-lighting conditions. Moreover, it is rather difficult to take high frame-rate videos due to the hardware limitations of conventional RGB-sensors. Therefore, our thesis mainly focuses on restoring high-quality (sharp, and high frame-rate) images and videos, from the low-quality (blur, and low frame-rate) ones for better practical applications. In this thesis, we mainly address the problem of how to restore a sharp image from a blurred stereo video sequence, a blurred RGB-D image, or a single blurred image. Then, by utilizing the faithful information about the motion provided by blurry effects in the image, we reconstruct high frame-rate and sharp videos based on an event camera, that brings blurry frame alive.
Stereo camera systems can provide motion information incorporated to help to remove complex spatially-varying motion blur in dynamic scenes. Given consecutive blurred stereo video frames, we recover the latent images, estimate the 3D scene flow, and segment the multiple moving objects simultaneously. We represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. These three tasks are naturally connected under our model and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes).
To tackle the challenging, minimal image deblurring case, namely, single-image deblurring, we first focus on blur caused by camera shake during the exposure time. We propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur by exploiting their underlying geometric relationships, with a single blurred RGB-D image as input. We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem solved in an alternative manner.
In general cases, we solve the single-image deblurring task by studying the problem in the frequency domain. We show that the auto-correlation of the absolute phase-only image (phase-only image means the image is reconstructed only from the phase information of the blurry image) can provide faithful information about the motion (e.g., the motion direction and magnitude) that caused the blur, leading to a new and efficient blur kernel estimation approach.
Event cameras are gaining attention for they measure intensity changes (called `events') with microsecond accuracy. The event camera allows the simultaneous output of the intensity frames. However, the images are captured at a relatively low frame-rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we model the blur-generation process by associating event data to a latent image. We propose a simple and effective approach, the EDI model, to reconstruct a high frame-rate, sharp video (>1000 fps) from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable.
Then, we improved the EDI model by using multiple images and their events to handle flickering effects and noise in the generated video. Also, we provide a more efficient solver to minimize the proposed energy model.
Last, the blurred image and events also contribute to optical flow estimation. We propose a single image and events based optical flow estimation approach to unlock their potential applications.
In summary, this thesis addresses how to recover sharp images from blurred ones and reconstruct a high temporal resolution video from a single image and event. Our extensive experimental results demonstrate our proposed methods outperform the state-of-the-art
