Abstract — In many imaging systems, the detector array is not sufficiently dense to adequately sample the scene with the desired field of view. This is particularly true for many infrared focal plane arrays. Thus, the resulting images may be severely aliased. This paper examines a technique for estimating a high-resolution image, with reduced aliasing, from a sequence of undersampled frames. Several approaches to this problem have been investigated previously. However, in this paper a maximum a posteriori (MAP) framework for jointly estimating image registration parameters and the high-resolution image is presented. Several previous approaches have relied on knowing the registration parameters a priori or have utilized registration techniques not specifically designed to treat severely aliased images. In the proposed method, the registration parameters are iteratively updated along with the high-resolution image in a cyclic coordinate-descent optimization procedure. Experimental results are provided to illustrate the performance of the proposed MAP algorithm using both visible and infrared images. Quantitative error analysis is provided and several images are shown for subjective evaluation. Index Terms—Aliased, high resolution, image registration, image sequence, MAP estimation. I.

A new method named STM is described for determining distance of objects and rapid autofocusing of camera systems. STM uses image defocus information and is based on a new Spatial-Domain Convolution/Deconvolution Transform. The method requires only two images taken with dierent camera parameters such as lens position, focal length, and aperture diameter. Both images can be arbitrarily blurred and neither of them needs to be a focused image. Therefore STM is very fast in comparison with Depth-from-Focus methods which search for the lens position or focal length of best focus. The method involves simple local operations and can be easily implemented in parallel to obtain the depthmap of a scene. STM has been implemented on an actual camera system named SPARCS. Experiments on the performance of STM and their results on realworld planar objects are presented. The results indicate that the accuracy of STM compares well with Depth-from-Focus methods and is useful in practical ap...

The standard methods for extracting range data from optical triangulation scanners are accurate only for planar objects of uniform reflectance illuminated by an incoherent source. Using these methods, curved surfaces, discontinuous surfaces, and surfaces of varying reflectance cause systematic distortions of the range data. Coherent light sources such as lasers introduce speckle artifacts that further degrade the data. We present a new ranging method based on analyzing the time evolution of the structured light reflections. Using our spacetime analysis, we can correct for each of these artifacts, thereby attaining significantly higher accuracy using existing technology. We present results that demonstrate the validity of our method using a commercial laser stripe triangulation scanner. 1 Introduction Active optical triangulation is one of the most common methods for acquiring range data. Although this technology has been in use for over twenty years, its speedandaccuracyhas increasedd...

A new method is described for recovering the distance of objects in a scene from images formed by lenses. The recovery is based on measuring the change in the scene's image due to a known change in the three intrinsic camera parameters: (i) distance between the lens and the image detector, (ii) focal length of the lens, and (iii) diameter of the lens aperture. The method is parallel involving simple local computations. In comparison with stereo vision and structure-frommotion methods, the correspondence problem does not arise. This method for depth-map recovery may also be used for (i) obtaining focused images (i.e. images having large depth of field) from two images having finite depth of field, and (ii) rapid autofocusing of computer controlled video cameras. 1. Introduction Here we describe a new passive ranging method which in principle is fast and involves relatively weak assumptions that are generally valid. The method is basically a generalized version of the `depth-from-focu...

We present a signal-processing framework for light transport. We study the frequency content of radiance and how it is altered by phenomena such as shading, occlusion, and transport. This extends previous work that considered either spatial or angular dimensions, and it offers a comprehensive treatment of both space and angle. We show that occlusion, a multiplication in the primal, amounts in the Fourier domain to a convolution by the spectrum of the blocker. Propagation corresponds to a shear in the space-angle frequency domain, while reflection on curved objects performs a different shear along the angular frequency axis. As shown by previous work, reflection is a convolution in the primal and therefore a multiplication in the Fourier domain. Our work shows how the spatial components of lighting are affected by this angular convolution. Our framework predicts the characteristics of interactions such as caustics and the disappearance of the shadows of small features. Predictions on the frequency content can then be used to control sampling rates for rendering. Other potential applications include precomputed radiance transfer and inverse rendering.

INTRODUCTION Pictorial pattern recognition systems are often described as consisting of three parts: a preprocessing part, a feature extraction part, and a classification part. The preprocessing is used to enhance or sharpen the image to be processed. This is usually done using linear operations or operations on the gray scale such as thresholding [1-3] The classification part is fairly well understood [4,5]. The feature extractor, on the other hand, is very much dependent upon the actual problem and no general theory has emerged on how to deal with it. Feature extraction procedures so far have been ad hoc, often referred to as "a bag of tricks." The present work grew out of an interest in finding a single picture operator that could in parallel perform a number of useful operations and that could work on several levels in a hierarchy. One background to this interest is the feeling that the eyes and brains of humans and animals are likely to have such standard operators, as the micro

We present the initial results of a novel technique that uses only phase information from a digital hologram for the reconstruction of three-dimensional (3D) objects. Our holograms are created using phase-shift digital holography. Perspectives of the 3D object are usually reconstructed numerically on a computer. For large holograms this can be a computationally intensive task. We believe that the proposed reconstruction technique is promising for 3D display because the phase-encoded digital holograms admit optical, and therefore realtime, reconstructions that use commercially available display devices such as liquid crystal spatial light modulators. Numerical evaluation of the reconstructed 3D object and an experimental demonstration are presented.

In this paper, we introduce the notion of a programmable imaging system. Such an imaging system provides a human user or a vision system significant control over the radiometric and geometric characteristics of the system. This flexibility is achieved using a programmable array of micro-mirrors. The orientations of the mirrors of the array can be controlled with high precision over space and time. This enables the system to select and modulate rays from the light field based on the needs of the application at hand. We have implemented a programmable imaging system that uses a digital micro-mirror device (DMD), which is used in digital light processing. Although the mirrors of this device can only be positioned in one of two

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

The image of a scene formed by an optical system such as a lens contains both photometric and geometric information about the scene. `Inverse Optics' is the problem of recovering this information from a set of images sensed by the camera. Previous solutions to this problem-- the depth-from-focusing methods-- required a large number (in principle, infinitely many) of images to be recorded and processed. Hence the methods were slow and computationally intensive. Recent work in this area suggests solutions that require only a few images and therefore are fast and computationally efficient. Here we present a coherent view of recent developments. Theoretical principles, practical issues, and unsolved problems are discussed. Preliminary experimental results are presented.