Morphological hit-or-miss transform for binary and gray-tone image processing and its optical implementation

Fuzzy-reasoning theory is widely used in industrial control. Mathematical morphology is a powerful tool to perform image processing. We apply fuzzy-reasoning theory to morphology and suggest a scheme of fuzzy-reasoning morphology, including fuzzy-reasoning dilation and erosion functions. These functions retain more fine details than the corresponding conventional morphological operators with the same structuring element. An optical implementation has been developed with area-coding and thresholding methods.

A new nonlinear spatial encoding method for cascadable fuzzy logic

A multi-agent framework inspired by natural and physical systems is presented for data discovery and image enhancement. The input image is represented as a topographic landscape upon which a large population of independent simple, reactive, mobile agents resides. The local landscape configuration presents stimuli to each agent, influencing agent behaviour and resulting in changes in agent orientation and movement. Individual agents deposit a trail as they move and leave specific marks in response to stimuli above a certain threshold. The parallel interactions of the agent population and the image landscape result in emergent patterns of trails and marks being generated, corresponding to global (population level) perception of the original image. The emergent patterns exhibit image feature extraction and represent an indirect processing of the input image. External environmental pressures may be applied to the emergent patterns to further amplify the feature extraction. The framework represents a decentralised approach to image enhancement which is extensible. Different types of agent may be developed to perform different image processing functions, or other problems whose definition and solution may be represented as spatial patterns. Results including binary image processing, greyscale enhancement, colour image processing and related spatial processing problems are presented.

An entropy network mapped from a binary feature tree is described and compared with the Hamming net. Implementation of the entropy network includes extracting features from an input and making the decision according to these features. The feature extraction in the first layer of the entropy network is implemented by an optical method. Optical implementation with an incoherent shadow casting correlation system is proposed, and experimental demonstrations are given. The processing capability of the proposed optical system is discussed.

In this paper we present a two-dimensional cellular hypercube architecture for image processing that combines features of the conventional hypercube and cellular logic architectures for 2-D computation cells. A unified theory of parallel binary image processing, binary image algebra (BIA), serves as a software tool for designing parallel image processing algorithms. To match the hardware to the software, we characterize the cellular processors using the same algebraic structure as BIA. The two-dimensional cellular hypercube image processor is a cellular SIMD machine with N2 cells and has a simple overall organization, low cell complexity and fast processing ability. An optical cellular hypercube implementation of BIA is proposed which offers parallel input/output and global interconnection capabilities which are difficult to do in planar VLSI technology.

Designing a pure phase multifunctional diffractive optical element (M-DOE) is a challenging task, as the regular summation of multiple pure phase functions results in a complex function. One of the widely used multiplexing methods to design a pure phase M-DOE is the random multiplexing method. In this method, different pure phase functions are multiplied to mutually exclusive binary random functions before summation. However, M-DOEs designed using the random multiplexing method are prone to scattering noise. In this study, a novel approach based on a modified Gerchberg-Saxton algorithm (GSA) has been proposed and demonstrated for the design of pure-phase multifunctional DOEs. In this approach, the complex M-DOE obtained by regular summation is used as a reference, and with suitable constraints, the amplitude component of the complex M-DOE is transported into the phase component, resulting in a pure phase MDOE. This modified algorithm is called Transport of Amplitude into Phase based on GSA (TAP-GSA). This method has been demonstrated on a well-established incoherent digital holography technique called Fresnel incoherent correlation holography (FINCH). In FINCH, it is necessary to multiplex two-phase masks, which can be achieved using random multiplexing or polarization multiplexing, resulting in reconstruction noise and low light throughput, respectively. Under low-light conditions, random multiplexing is a better choice than the polarization multiplexing method. The M-DOE designed using TAP-GSA for FINCH improved the light throughput and exhibited a higher SNR in comparison to the random multiplexing method.

In this paper, we presented a scheme for complex-valued modular multiplication based on digital partitioning technique. The module (-2)N-1 is introduced for its simple arithmetic. Both of the operands joining in the complex number multiplication are treated as two N-bit real numbers. Because of its advantage in expressing number, the negabinary number system is adopted. The subtraction in complex number multiplication is converted to addition operation by weighting shifting. Although the direct result takes the looks of a serial of residues, we proposed an algorithm for reconstructing the complex-valued multiplication produce by studying the product decomposing rules. The algorithm can be implemented in parallel manner, we studied the optical implementation based on an incoherent optical correlator and some electronic devices as auxiliary for reconstructing the product. Both the advantage and the disadvantage are analyzed. And the comparison with other optical complex-valued multiplication algorithms is also presented.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An optoelectronic morphological processor for industrial on- line inspection is presented in this paper. The principle of the processor is based on the morphological hit-miss transform. By using an extensive complementary encoding method, which combines the foreground and background of an image into a whole, the hit-miss transform, which usually needs three steps to perform, can be implemented in only one step. The optical implementation hardware is based on an incoherent optical correlator due to its compactness in structure and immunity to coherent noise. The experimental results are given.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Fast parallel processing of gray-scale images and exact hard-clip thresholding are two important functionalities necessary in optoelectronic implementations of structural processors. The parallel nature of processing stems from optical implementation of local operations with arrays of active smart pixels. We have demonstrated a morphological image processor composed of arrays of bistable optoelectronic transceivers which are connected in differential pairs and work as comparators. The use of differential pairs of optical thyristors fabricated in GaAs technology allows to realize a dual rail architecture for this photonic morphological image processor. The processor consists of a thresholding module and a binary morphological processing module. The thresholding module decomposes gray level images into series of binary slices. In the binary morphological processing module operations are performed within a neighborhood defined by a structuring element implemented as a diffractive fan-out element. In the prototype set-up we demonstrate median filtering, dilation and erosion operations performed for an image of 8x8 pixels and threshold decomposition of 6 gray level images. In principle all rank order as well as morphological filters can be optically calculated in the set-up. Additional functionality of the processor is achieved with use of the electronic layer with digital cellular processors. The electronic layer, designed as an array of simple digital processors, realizes a set of operations on binary images using 4 bit programmable weights. Simulation results for 0.8 µm CMOS technology are presented. We discuss the limitations of the photonic morphological image processing with respect to bandwidth, parallelism and architecture of the processor.