Image analysis – Image enhancement or restoration
Reexamination Certificate
1999-11-30
2002-03-19
Rogers, Scott (Department: 2624)
Image analysis
Image enhancement or restoration
C382S260000, C382S261000, C382S275000
Reexamination Certificate
active
06360021
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention is generally related to the field of analog and digital signal processing, and more particularly, to apparatus and methods for the efficient representation and processing of signal or image data.
2. Description of the Prior Art
FIG. 1
is a block diagram of a typical prior art signal processing system
100
. As shown in the figure, such systems typically include an input stage
102
, a processing stage
104
, an output stage
106
, and data storage element(s)
108
.
Input stage
102
may include elements such as sensors, transducers, receivers, or means of reading data from a storage element. The input stage provides data which are informative of man-made and/or naturally occurring phenomena. The informative component of the data may be masked or contaminated by the presence of an unwanted signal, which is usually characterized as noise. In some applications, an input element may be employed to provide additional control of the input or processing stages by a user, a feedback loop, or an external source.
The input data, in the form of a data stream, array, or packet, may be presented to the processing stage directly or through an intermediate storage element
108
in accordance with a predefined transfer protocol. Processing stage
104
may take the form of dedicated analog or digital devices, or programmable devices such as central processing units (CPUs), digital signal processors (DSPs), or field programmable gate arrays (FPGAs) to execute a desired set of data processing operations. Processing stage
104
may also include one or more CODECs (COder/DECcoders).
Output stage
106
produces a signal, display, or other response which is capable of affecting a user or external apparatus. Typically, an output device is employed to generate an indicator signal, a display, a hardcopy, a representation of processed data in storage, or to initiate transmission of data to a remote site, for example. It may also be employed to provide an intermediate signal for use in subsequent processing operations and/or as a control element in the control of processing operations.
When employed, storage element
108
may be either permanent, such as photographic film and read-only media, or volatile, such as dynamic random access memory (RAM). It is not uncommon for a single signal processing system to include several types of storage elements, with the elements having various relationships to the input, processing, and output stages. Examples of such storage elements include input buffers, output buffers, and processing caches.
The primary objective of signal or information processing system
100
is to process input data to produce an output which is meaningful for a specific application. In order to accomplish this goal, a variety of processing operations may be utilized, including noise reduction or cancellation, feature extraction, data categorization, event detection, editing, data selection, and data re-coding.
The design of a signal processing system is influenced by the intended use of the system and the expected characteristics of the source signal used as an input. In most cases, the performance efficiency required, which is affected by the available storage capacity and computational complexity of a particular application, will also be a significant design factor.
In some cases, the characteristics of the source signal can adversely impact the goal of efficient data processing. Except for applications in which the input data are naturally or deliberately constrained to have narrowly definable characteristics (such as a limited set of symbol values or a narrow bandwidth), intrinsic variability of the source data can be an obstacle to processing the data in a reliable and efficient manner without introducing errors arising from ad hoc engineering assumptions. In this regard, it is noted that many data sources which produce poorly constrained data are of importance to people, such as sound and visual images.
Conventional image processing methods suffer from a number of inefficiencies which are manifested in the form of slow data communication speeds, large storage requirements, and disturbing perceptual artifacts. These can be serious problems because of the variety of ways it is desired to use and manipulate image data, and because of the innate sensitivity people have for visual information.
Specifically, an “optimal” image or signal processing system would be characterized by, among other things, swift, efficient, reliable, and robust methods for performing a desired set of processing operations. Such operations include the transduction, storage, transmission, display, compression, editing, encryption, enhancement, sorting, categorization, feature detection and recognition, and aesthetic transformation of data, and integration of such processed data with other information sources. Equally important, in the case of an image processing system, the outputs should be capable of interacting with human vision as naturally as possible by avoiding the introduction of perceptual distractions and distortion.
That a signal processing method should be robust means that its speed, efficiency, and quality (for example), should not depend strongly on the specifics of any particular characteristics of the input data, i.e., it should perform “optimally,” or near that level, for any plausible input.
This is an important aspect because a common inadequacy suffered by signal processing methods is their failure to be robust. JPEG-type methods in imaging, for example, perform better for “photographic” images having gentle gradations in color and luminance than for graphic images and others having sharp discontinuities. On the other hand, image compression methods such as those embodied in the GIF format perform best when an image has few of the complexities found in photographic images. Similar examples may be cited with regard to processing operations performed on audio and other classes of input data.
In part, conventional image processing methods lack robustness because there are an infinite number of possible images. Adding to this is the complication that in most situations, it is impossible to know beforehand exactly what features and complexities an image will possess. Thus, to describe an image entirely, one approach is to determine the luminance and color of every point in the image. However, the volume of information needed to accomplish this task can exceed several megabytes for a digital image of moderate size, making it burdensome to store, process, and transmit such information. Even then, the digital representation is an inexact record of the original image owing to the limitations inherent in constructing binary value based representations of continuous analog signals.
Information is lost in any discrete representation of continuous-valued data because discrete sampling over any finite duration or area cannot capture all of the variations in the source data. Similarly, information is lost in any quantization process when the full range of values in the source data cannot be represented by a set of discrete values.
In addition to difficulties imposed by the nature or implementation of a processing operation, other problems must be addressed when contaminating noise sources mask or distort the component of an input that is assumed to represent a signal of interest. However, it is rarely appreciated that there are other forms of randomness and unpredictability which cannot be defined legitimately as noise but which are nonetheless the source of problems with regard to the quality and robustness of signal processing methods. These forms of unpredictability may be considered in terms of intrinsic randomness and ensemble variability. Intrinsic randomness refers to randomness that is inseparable from the medium or source of data. The quantal randomness of photon capture is an example of intrinsic randomness.
Ensemble variability refers to any unpredictability in a class of data or information sources. Data representative of
McCarthy Sean T.
Owen William G.
Flehr Hohbach Test Albritton & Herbert LLP
Rogers Scott
The Regents of the University of California
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