Shuqun Zhang

A new impulse detector for switching median filters

A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators. Extensive simulations show that the proposed filter provides better performance than many of the existing switching median filters with comparable computational complexity. In particular, the proposed filter is directed toward improved line preservation.

Color image encryption using double random phase encoding

A new encryption technique is proposed to encrypt color images using existing optical encryption systems for gray-scale images. The color images are converted to their indexed image formats before they are encoded. At the decryption end, the color images are recovered by converting the decrypted indexed images back to their RGB formats. The proposed single-channel color image encryption method is more compact and robust than the multichannels methods. Since color information is added to the shape information, better verification performance can be achieved in optical security systems.xa0©1999 John Wiley & Sons, Inc. Microwave Opt Technol Lett 21: 318–323, 1999.

Boundary-based image segmentation using binary level set method

A novel binary level set method for boundary-based image segmentation is proposed, which is extended from region-based binary level set methods. The proposed binary level set method is based on the geometric active contour framework, which is a traditional level set method applied in boundary-based image segmentation. However, being different from the geometric active contour, the proposed binary level set method replaces the traditional level set function with a binary level set function to reduce the expensive computational cost of redistancing the traditional level set function. The experiments and complexity analysis show that the proposed binary level set method is more efficient than the geometric active contour for image segmentation while giving similar results to the geometric active contour.

Illumination-invariant pattern recognition with joint-transform-correlator-based morphological correlation.

The performance of nonlinear morphological correlation is investigated and compared with that of conventional linear correlation. In particular, the effects of illumination variations on the morphological correlation output are investigated in detail. The morphological correlation is shown to be invariant to uniform input-image illumination when the input-image illumination is higher than that of the reference. It also provides higher pattern discriminability, sharper peaks, and more-robust detection in the presence of salt-and-pepper noise than does the linear correlation. Computer-simulation results are provided.

High-security optical integrated stream ciphers

We propose a new optical stream cipher for security applica- tions. The stream ciphers security is enhanced using hybrid cellular au- tomata to replace uniform cellular automata as the keystream genera- tors. The encoded ciphertext is obtained by randomly encoding both the initial states of cellular automata and their evolution rules. This results in an increase in complexity to crack the keystream generator and, thus, enhances the security of stream ciphers. The hardware implementation can be also correspondingly reduced. An optical solid-integrated scheme is suggested to implement the proposed stream cipher for high-speed encryption and decryption.

Gradient vector flow active contours with prior directional information

Active contours, or snakes, have been widely used in image processing and computer vision for image segmentation and object tracking. However, they usually have poor performance in segmenting images with complex object shape and complex background, and also in dealing with the issue of weak-edge-leakage. To guide the front of active contour toward the desired object boundary and prevent it from moving over the weak edges with strong neighbors, we present a novel external force field, referred to as gradient and direction vector flow (G&DVF), which integrates the gradient vector flow (GVF) and the prior directional information provided by a user. The proposed method is sufficiently general and simple to implement. The experiments conducted on image segmentation demonstrate that the proposed method is insensitive to image clutters/noise and capable of driving the fronts of active contours to conform to complex shapes and addressing the issue of weak-edge-leakage in some cases.

Modification of standard image compression methods for correlation-based pattern recognition

Standard image compression algorithms may not perform well in compressing images for pattern recognition applications, since they aim at retaining image fidelity in terms of perceptual quality rather than preserving spectrally significant information for pattern recognition. New compression algorithms for pattern recognition are therefore investigated, which are based on the modification of the standard compression algorithms to simultaneously achieve higher compression ratio and improved pattern recognition performance. This is done by emphasizing middle and high frequencies and discarding low frequencies according to a new distortion measure for compression. The operations of denoising, edge enhancement, and compression can be integrated in the same encoding process in the proposed compression algorithms. Simulation results show the effectiveness of the proposed compression algorithms.

Efficient Illumination Insensitive Object Tracking by Normalized Gradient Matching

We propose a novel tracking algorithm by minimizing the sum-of-squared differences (SSD) between the normalized image gradients of the template image and the input image from the test image sequence. The proposed tracking algorithm is efficient to implement since it is based on the framework of the inverse compositional algorithm, a computationally efficient tracking technique. The experiments show that the proposed tracking algorithm is superior to the intensity-based inverse compositional algorithm in tracking objects under varying illumination conditions.

OPTICAL FUZZY IMAGE-PROCESSING BASED ON SHADOW-CASTING

Abstract An optical system based on an area-coded scheme and the shadow-casting technique is proposed for fuzzy image processing. All of the fuzzy logic functions of two images can be implemented in parallel. The access of the fuzzy logic functions can be easily achieved by programming an LED source array in the system. Above all, no thresholding device is required. The experimental results are also given.

Application of super-resolution image reconstruction to digital holography

We describe a new application of super-resolution image reconstruction to digital holography which is a technique for three-dimensional information recording and reconstruction. Digital holography has suffered from the low resolution of CCD sensors, which significantly limits the size of objects that can be recorded. The existing solution to this problem is to use optics to bandlimit the object to be recorded, which can cause the loss of details. Here super-resolution image reconstruction is proposed to be applied in enhancing the spatial resolution of digital holograms. By introducing a global camera translation before sampling, a high-resolution hologram can be reconstructed from a set of undersampled hologram images. This permits the recording of larger objects and reduces the distance between the object and the hologram. Practical results from real and simulated holograms are presented to demonstrate the feasibility of the proposed technique.