MULTI-FRAME OPTICAL FLOW ESTIMATION USING SPATIO-TEMPORAL TRANSFORMERS

Optical flow estimation is a computer vision problem which aims to estimate apparent 2D motion (flow velocities) of image intensities between two or more consecutive frames in an image sequence. Optical flow information is useful for quantifying dense motion field in numerous applications such as autonomous driving, object tracking in traffic control systems, video frame interpolation, video compression and structural biomarker development for medical diagnosis. Recent state of the art learning methods for optical flow estimation are two-frame based methods where optical flow is estimated sequentially for each image pairs in an image sequence. In this work, we introduce a learning based spatio-temporal transformers for multi-frame optical flow estimation (SSTMs). SSTM is a multi-frame based optical flow estimation algorithm which can learn and estimate non-linear motion dynamics in a scene from multiple sequential images of the scene. When compared to two-frame methods, SSTM can provide improved optical flow estimates in regions with object occlusions and near boundaries where objects may enter or leave the scene (out-of-boundary regions). Our method utilizes 3D Convolutional Gated Recurrent Networks (3D-ConvGRUs) and space-time attention modules to learn the recurrent space-time dynamics of input scenes and provide a generalized optical flow estimation. When trained using the same training datasets, our method outperforms both the existing multi-frame based optical flow estimation algorithms and the recent state of the art two-frame methods on Sintel benchmark dataset (based on a computer-animated movie) and KITTI 2015 driving benchmark datasets

Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008