Showing papers on "Motion blur published in 1996"

Motion Deblurring and Super-resolution from an Image Sequence

Abstract: In many applications, like surveillance, image sequences are of poor quality. Motion blur in particular introduces significant image degradation. An interesting challenge is to merge these many images into one high-quality, estimated still. We propose a method to achieve this. Firstly, an object of interest is tracked through the sequence using region based matching. Secondly, degradation of images is modelled in terms of pixel sampling, defocus blur and motion blur. Motion blur direction and magnitude are estimated from tracked displacements. Finally, a high-resolution deblurred image is reconstructed. The approach is illustrated with video sequences of moving people and blurred script.

Image motion estimation from motion smear-a new computational model

Abstract: Motion smear is an important visual cue for motion perception by the human vision system (HVS). However, in image analysis research, exploiting motion smear has been largely ignored. Rather, motion smear is usually considered as a degradation of images that needs to be removed. In this paper, the authors establish a computational model that estimates image motion from motion smear information-"motion from smear". In many real situations, the shutter of the sensing camera must be kept open long enough to produce images of adequate signal-to-noise ratio (SNR), resulting in significant motion smear in images. The authors present a new motion blur model and an algorithm that enables unique estimation of image motion. A prototype sensor system that exploits the new motion blur model has been built to acquire data for "motion-from-smear". Experimental results on images with both simulated smear and real smear, using the authors' "motion-from-smear" algorithm as well as a conventional motion estimation technique, are provided. The authors also show that temporal aliasing does not affect "motion-from-smear" to the same degree as it does algorithms that use displacement as a cue. "Motion-from-smear" provides an additional tool for motion estimation and effectively complements the existing techniques when apparent motion smear is present.

Optical flow recognition from the power spectrum of a single blurred image

Abstract: In this paper a new technique for calculation of the optical flow is presented. When there is motion in the observed scene, an image taken will be motion blurred (to a degree depending on the exposure time). Up to now most of the algorithms for estimating the motion in a scene ignored motion blur and treated it as noise. On the contrary, motion blur is structured information and in certain cases can be used to infer the velocities locally. This new approach uses the information of the motion blur in the frequency domain to extract the orientation and the magnitude of the velocity-optical flow.

Steerable Filters and Cepstral Analysis for Optical Flow Calculation from a Single Blurred Image

Abstract: This paper considers the explicit use of motion blur to compute the Optical Flow. In the past, many algorithms have been proposed for estimating the relative velocity from one or more images. The motion blur is generally considered an extra source of noise and is eliminated, or is assumed nonexistent. Unlike most of these approaches, it is feasible to estimate the Optical Flow map using only the information encoded in the motion blur. An algorithm that estimates the velocity vector of an image patch using the motion blur only is presented; all the required information comes from the frequency domain. The rst step consists of using the response of a family of steerable lters applied on the log of the Power Spectrum in order to calculate the orientation of the velocity vector. The second step uses a technique called Cepstral Analysis. More precisely, the log power spectrum is treated as another signal and we examine the Inverse Fourier Transform of it in order to estimate the magnitude of the velocity vector. Experiments have been conducted on articially blurred images and with real world data. 1

Recognition of images degraded by linear motion blur without restoration

Abstract: The paper is devoted to the feature-based description of images degraded by linear motion blur. The proposed features are invariant with respect to motion velocity, are based on image moments and are calculated directly from the blurred image. In that way, we are able to describe the original image without the PSF identification and image restoration. In many applications (such as in image recognition against a database) our approach is much more effective than the traditional blind-restoration one. The derivation of the motion blur invariants is a major theoretical result of the paper. Numerical experiments are presented to illustrate the utilization of the invariants for blurred image description. Stability of the invariants with respect to additive random noise is also discussed and is shown to be sufficiently high. Finally, another set of features which are invariant not only to motion velocity but also to motion direction is introduced.

Interactive real-time motion blur

Abstract: Motion blurring of fast-moving objects is highly desirable for virtual environments and 3D user interfaces. However, all currently known algorithms for generating motion blur are too slow for inclusion in interactive 3D applications. We introduce a new motion-blur algorithm that works in three dimensions on a per object basis. The algorithm operates in real time even for complex objects consisting of several thousand polygons. While it only approximates true motion blur, the generated results are smooth and visually consistent. We achieve this performance break-through by taking advantage of hardware-assisted rendering of semitransparent polygons, a feature commonly available in today's workstations.

Regularized blur-assisted displacement field estimation

Abstract: Due to the finite acquisition time of practical cameras, objects can move during image acquisition, therefore introducing motion blur degradations. Traditionally, these degradations are treated as undesirable artifacts that should be removed before further processing. We consider the use of motion blur as an indication of scene motion. We present two robust regularized motion estimation algorithms that consider the use of (motion) blur in their formulation. The first algorithm uses motion blur as prior knowledge for the estimation of the motion field. The second algorithm considers the joint estimation of the motion and motion blur. Each approach results in a motion blur point spread field, a motion field and a restored image in an approach that is different from previous work. Preliminary results are presented.

Motion analysis of echocardiograph using optical flow method

Abstract: A gradient-based method is applied to analyze two echocardiograph sequence velocity field. The method assumes local spatial constancy of the optical flow and uses a weighted least-squares minimization to determine the optical flow. A hierarchical improvement to the method is proposed to solve the problems of motion blur and sensitivity to noise. Experimental results show that both the implemented method and the improvement to it are available to obtain global quality evaluation of the velocity field of the echocardiograph sequences.

A partitioned modified Hopfield neural network algorithm for real-time image restoration

Abstract: In this paper, two image partitioning schemes are examined. The first scheme examined avoids boundary conflicts by the use of four restoration phases. The second scheme examined requires a degree of synchronization of the processors restoring adjacent regions. Both schemes avoid conflicting boundary conditions by taking into account the local image formation properties. Without any loss of processing speed, or increase in the number of processors required to restore the image, synchronizing conditions are not required in the four-phase scheme to restore the image accurately, however can be used to maximize restoration efficiency. An improved modified Hopfield neural network-based algorithm is developed to be especially applicable to the problems of real-time image processing based on the described partitioning schemes. The proposed algorithm extends the concepts involved with previous algorithms to enable faster image processing and a greater scope for using the inherent parallelism of the neural network approach to image processing. The simulation in this investigation shows that the new algorithm is able to maximize the efficiency of the described partitioning methods. This paper also presents an example of an application of the proposed algorithm to restore images degraded by motion blur.

Three-Dimensional Modeling and Effects on Still Images

Abstract: Designers and creative artists use computer grapics and image processing effects on still photographs in application areas such as advertising, entertainment, broadrastinq and the arts. Most of the effects available in research and commercial work are two-dimensional in nature, for example image processing filters (blur, edge enhancement) and creative effects (tilings, reflections). There is almost no usage of information taken from the 3-D world in which the objects appearing in the image are located. In this paper we present a novel method for creating 3-D effects on photographs, or in general on any image created by rendering a 3-D world. The artist interacts with the image using a set of intuitive direct manipulation interface objects. These objects let the user define a 3-D model, display it, and manipulate it in a 3-D space which is correlated with that of the input image. The generated model can be an arbitrarily complex 3-D polyhedron. Any texture, including texture taken from the input photograph, can be mapped into any of its faces and used for special effects. We discuss and show examples for effects such as copy and paste, motion blur, model editing and deformations, lighting effects, and shadows.

Estimation of accelerated motion for motion-compensated frame interpolation

Abstract: In motion-compensated processing of image sequences, e.g. in frame interpolation, frame rate conversion, deinterlacing, motion blur correction, image sequence restoration, slow-motion replay, etc., the knowledge of motion is essential. In these applications motion information has to be determined from the image sequence. Most motion estimation algorithms use only a simple motion model, and assume linear constant speed motion. The contribution of our paper is the development of an algorithm for modeling and estimation of accelerated motion trajectories, based on a second order motion model. This model is more general and much closer to the real motion present in natural image sequences. The parameters of the accelerated motion are determined from two consecutive motion fields, that has been estimated from three consecutive image frames using a multiresolution pel-recursive Wiener-based motion estimation algorithm. The proposed algorithm was successfully tested on artificial image sequences with synthetic motion as well as on natural real-file videophone and videoconferencing sequences in a frame interpolation environment.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image sequence restoration for removing space-variant motion blur

Abstract: An image restoration algorithm for removing motion blur, which occurs in an image sequence or moving pictures, is proposed. More specifically, the proposed iterative restoration algorithm adaptively reduces nonuniform motion blur by using motion vector information from consecutive image fields. Motion vectors are estimated based on the well known block matching algorithm, and the corresponding blur model is embodied into the point spread function, which is used to implement the iterative image restoration algorithm. A blur model modification method is also proposed to reduce artifacts on the boundary area between objects with different blur patterns.