Alexander A. Sawchuk

Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lees local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lees algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.

Adaptive restoration of images with speckle

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.

Digital optical computing

This paper concerns binary digital computing systems in which the information-carrying medium consists entirely or primarily of photons. The paper begins with a review of analog, discrete, and binary methods of representing information in a computer, followed by a survey of many techniques for implementing binary combinatorial and sequential logic functions with individual optical devices and arrays of devices. Next is a discussion of communication, interconnection, and input-output problems of digital electronic and optical computers at the gate, chip, and processor level. A particular architecture for implementing general sequential optical logic systems including digital optical processors is described. This architecture avoids some of the interconnection problems of electronic integrated circuits and VLSI systems, and offers the potential of non von Neumann parallel digital processors. Finally, the current limitations and future needs of optical logic devices and digital optical computing systems are outlined.

Space-variant image restoration by coordinate transformations*

A method of image restoration for certain systems with space-variant point-spread functions is presented. The technique, called coordinate transformation restoration (CTR), is applicable to a large class of optical degradations, and operates by first transforming the degraded image by a geometrical distortion. Following this, space-invariant inverse filtering or estimation and another transformation are used to complete the process. The CTR technique makes restoration practical for a variety of incoherent systems with motion and aberration degradations by effectively reducing the system dimensionality.

Supervised textured image segmentation using feature smoothing and probabilistic relaxation techniques

A description is given of a supervised textured image segmentation algorithm that provides improved segmentation results. An improved method for extracting textured energy features in the feature extraction stage is described. It is based on an adaptive noise smoothing concept that takes the nonstationary nature of the problem into account. Texture energy features are first estimated using a window of small size to reduce the possibility of mixing statistics along region borders. The estimated texture energy feature values are smoothed by a quadrant filtering method to reduce the variability of the estimates while retaining the region border accuracy. The estimated feature values of each pixel are used by a Bayes classifier to make an initial probabilistic labeling. The spatial constraints are enforced through the use of a probabilistic relaxation algorithm. Two probabilistic relaxation algorithms are investigated. Limiting the probability labels by probability threshold is proposed. The tradeoff between efficiency and degradation of performed is studied. >

Architectural implications of a digital optical processor

A general technique is described for implementing sequential logic circuits optically. In contrast with semiconductor integrated circuitry, optical logic systems allow very flexible interconnections between gates and between subsystems. Because of this, certain processing algorithms which do not map well onto semiconductor architectures can be implemented on the optical structure. The algorithms and processor architectures which can be implemented on the optical system depend on the interconnection technique. We describe three interconnection methods and analyze their advantages and limitations.

Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l1 minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.

Adaptive Restoration Of Images With Speckle

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.

Space-variant image motion degradation and restoration

A description of motion degradation in linear incoherent optical systems is presented. Given a mechanical description of the motion, an equivalent linear space-variant system containing all the motion effects is derived, and detailed examples of common types of variant and invariant motion are included. Following a review of restoration techniques for motion blur, a method for image restoration applicable to a large class of space-variant systems is presented. This method is based on the decomposition of the degradation into geometrical coordinate distortions and a space-invariant operation. A computer simulation of space-variant restoration is included.

USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors

Many ubiquitous computing applications involve human activity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard benchmarks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others.