A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.

Super-resolution image reconstruction: a technical overview

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

Image coding using vector quantization: a review

The notion of perceptual coding, which is based on the concept of distortion masking by the signal being compressed, is developed. Progress in this field as a result of advances in classical coding theory, modeling of human perception, and digital signal processing, is described. It is proposed that fundamental limits in the science can be expressed by the semiquantitative concepts of perceptual entropy and the perceptual distortion-rate function, and current compression technology is examined in that framework. Problems and future research directions are summarized. >

Signal compression based on models of human perception

An approach to the analysis of dynamic facial images for the purposes of estimating and resynthesizing dynamic facial expressions is presented. The approach exploits a sophisticated generative model of the human face originally developed for realistic facial animation. The face model which may be simulated and rendered at interactive rates on a graphics workstation, incorporates a physics-based synthetic facial tissue and a set of anatomically motivated facial muscle actuators. The estimation of dynamical facial muscle contractions from video sequences of expressive human faces is considered. An estimation technique that uses deformable contour models (snakes) to track the nonrigid motions of facial features in video images is developed. The technique estimates muscle actuator controls with sufficient accuracy to permit the face model to resynthesize transient expressions. >

/pdf/analysis-and-synthesis-of-facial-image-sequences-using-3zkhndfpu8.pdf

Analysis and synthesis of facial image sequences using physical and anatomical models

The initial conception of a model-based analysis synthesis image coding (MBASIC) system is described and a construction method for a three-dimensional (3-D) facial model that includes synthesis methods for facial expressions is presented. The proposed MBASIC system is an image coding method that utilizes a 3-D model of the object which is to be reproduced. An input image is first analyzed and an output image using the 3-D model is then synthesized. A very low bit rate image transmission can be realized because the encoder sends only the required analysis parameters. Output images can be reconstructed without the noise corruption that reduces naturalness because the decoder synthesizes images from a similar 3-D model. In order to construct a 3-D model of a person's face, a method is developed which uses a 3-D wire frame face model. A full-face image is then projected onto this wire frame model. For the synthesis of facial expressions two different methods are proposed; a clip-and-paste method and a facial structure deformation method.

Model-based analysis synthesis image coding (MBASIC) system for a person's face

This paper describes VHD (very high definition) image media competing in spatial resolution with the familiar film media as a new digital image media concept, and discusses a problem of image acquisition as a key technology for achieving VHD image systems. Towards the development of a VHD image acquisition system, the work presents a new signal-processing based approach using multiple different cameras. The image acquisition approach, processing and integrating multiple images taken simultaneously with multiple different cameras, produces an improved spatial resolution image with sufficiently high signal-to-noise ratio. Theoretical analysis and experimental simulations show clearly the improvement in the high frequencies and details.

Signal-processing based method for acquiring very high resolution images with multiple cameras and its theoretical analysis

Towards the development of a very high definition (VHD) image acquisition system, previously we developed the signal processing based approach with multiple cameras. The approach produces an improved resolution image with sufficiently high signal-to-noise ratio by processing and integrating multiple images taken simultaneously with multiple cameras. Originally, in this approach, we used multiple cameras with the same pixel aperture, but in this case there are severe limitations both in the arrangement of multiple cameras and in the configuration of the scene, in order to guarantee the spatial uniformity of the resultant resolution. To overcome this difficulty completely, this work presents the utilization of multiple cameras with different pixel apertures, and develops a new, alternately iterative signal processing algorithm available in the different aperture case. Experimental simulations clearly show that the utilization of multiple different-aperture cameras prospects to be good and that the alternately iterative algorithm behaves satisfactorily.

Takahiro Saito

Papers

Model-based analysis synthesis image coding (MBASIC) system for a person's face

Signal-processing based method for acquiring very high resolution images with multiple cameras and its theoretical analysis

Very high resolution imaging scheme with multiple different-aperture cameras

Subband Image Coding with Biorthogonal Wavelets

Model-Based Analysis Synthesis Coding of Videotelephone Images--Conception and Basic Study of Intelligent Image Coding--