Showing papers on "Motion blur published in 1990"

The accumulation buffer: hardware support for high-quality rendering

Abstract: This paper describes a system architecture that supports realtime generation of complex images, efficient generation of extremely high-quality images, and a smooth trade-off between the two.Based on the paradigm of integration, the architecture extends a state-of-the-art rendering system with an additional high-precision image buffer. This additional buffer, called the Accumulation Buffer, is used to integrate images that are rendered into the framebuffer. While originally conceived as a solution to the problem of aliasing, the Accumulation Buffer provides a general solution to the problems of motion blur and depth-of-field as well.Because the architecture is a direct extension of current workstation rendering technology, we begin by discussing the performance and quality characteristics of that technology. The problem of spatial aliasing is then discussed, and the Accumulation Buffer is shown to be a desirable solution. Finally the generality of the Accumulation Buffer is explored, concentrating on its application to the problems of motion blur, depth-of-field, and soft shadows.

Review of image-blur models in a photographic system using the principles of optics

Abstract: When a photographic imaging system has a focus error or a motion blur, it is not possible to correct the error by conventional optical printing. Digital image processing offers a chance to restore the blurred images by reversing the blurring processes under certain constraints. Although restoration algorithms vary in detail, they all need a model of the image-blurring process. This paper reviews image-blur models of each individual component in an image chain consisting of camera, film, and scanner. The models include image blur caused by focus error, camera shutter, object motion, light scattering in the film emulsion, film interimage effect, scanner optics, and scanner aperture. The emphasis is on mathematical simplicity and practical usefulness.

Quantitative analysis of artifacts in linear space-invariant image restoration

Abstract: Several image restoration algorithms exist in the literature ranging from deterministic iterative techniques to optimum recursive methods. Unfortunately, all these algorithms produce undesirable artifacts in the process of undoing the degradations because of the ill-posed nature of the image restoration problem. This paper provides a complete quantitative analysis of different artifacts caused by linear shift-invariant (LSI) image restoration methods. The aim of this paper is to mathematically show how these artifacts originate in the general case of an arbitrary blur point spread function and an arbitrary LSI restoration filter, and then to study the characteristics of these artifacts in the special cases of uniform motion blur and out-of-focus blur via experimental analysis. Several pictures that illustrate these artifacts are presented. We discuss strategies for the suppression of these artifacts based on the analysis provided.

Polygon-based post-process motion blur

Abstract: This paper discusses a three-pass raster motion-blur algorithm (and some generalizations) in the context of texture-mapped polygons and its application to blurring objects and surfaces made up of multiple polygons, which may move in different directions.

Optimal Convergence Rates in Signal Recovery

Abstract: In the context of image analysis, the method of Fourier-domain processing is shown to yield restored signals of optimal quality. This confirms conjectures of statistical optimality that have been made in the past. Quality is measured in terms of convergence rates, and the influence of image smoothness on convergence rates is quantified. This influence is particularly interesting in the case of motion blur, where there is a critical degree of image smoothness (approximately four derivatives of the image) beyond which no improvement in restored image quality may be obtained by passing to smoother images. That is in marked contrast to the case of out-of-focus blur, where restored image quality is always greater for smoother images.

Neural nets for image restoration

Abstract: No imaging system in practice is perfect, in fact the recorded images are always distorted or of finite resolution. An image recording system can be modeled by a Fredholm integral equation of the first kind. An inversion of the kernel representing the system, in the presence of noise, is an ill posed problem. The direct inversion often yields an unacceptable solution. In this paper, we suggest an Artificial Neural Network (ANN) architecture to solve ill posed problems in the presence of noise. We use two types of neuron like processing units: the units that use the weighted sum and the units that use the weighted product. The weights in the model are initialized using the eigen vectors of the kernel matrix that characterizes the recording system. We assume the solution to be a sample function of a wide sense stationary process with a known auto-correlation function. As an illustration, we consider two images that are degraded by motion blur.

A morphological approach to moving-object recognition with applications to machine vision

Abstract: A methodology based on mathematical morphology is proposed for both shape recognition and motion estimation of two-dimensional (2-D) objects or shapes. This novel approach is based on the introduction of a shape descriptor called the Morphological Autocorrelation Transform or MAT. The MAT of an image is composed of a family of Geometrical Correlation Functions (GCFs) which define the morphological covariance in a specific direction. The MAT is shown to be translation-, scale-, and rotation-invariant. Also, in most situations, a small subset of the MAT suffices for image representation. First, the characteristics and performance of a shape-recognition system based on the MAT are investigated and analyzed. A criterion based on the area under the GCF curve provides promising results. Computational complexity of the proposed system is examined. It is shown that important shape properties, such as area, perimeter, and orientation, are readily derived from the MAT representation. Second, a new algorithm for motion-parameter estimation based on the family of GCFs is developed. Under relatively weak conditions, it provides a very fast and effective way of estimating the speed and direction of a moving-object. Its computational complexity is studied. Experimentally, it is shown to work well for relatively fast-moving objects. Third, new high-speed architectures are proposed for efficient realization of the proposed schemes. Specifically, a Nonlinear Pipeline Processor (NPP) has been created to implement the two basic morphological transformations: dilation and erosion. NPP, a basic building block which can be used to realize the MAT, is highly modular and well-suited for VLSI implementation. Finally, the proposed integrated scheme is applied to the conveyor-belt problem in flexible automation. Experiments performed in the Computer-Integrated-Manufacturing (CIM) Laboratory of the Department of Mechanical Engineering at the University of Toronto demonstrated that the proposed algorithm works very well in spite of noise, motion blur and mechanical vibration.

Visual motion compensation using a neural shifter circuit

Abstract: It was recently proposed that a neural shifter circuit could be responsible for compensating for image motion so as to produce an unblurred image in visual cortex. An analysis of the motion blur problem in the frequency domain is presented, and it is shown how a neural shifter circuit behaves as a filter within this domain in order to extract spatial information from moving images. From this analysis it is possible to formally compare the shifter circuit to other computational strategies for reducing motion blur, such as spatio-temporal filters. A control systems analysis of the shifter circuit that allows one to predict how neural time delays affect the performance of the system is provided. The results of a computer simulation of a shifter circuit are presented. >