Dr. Wei Zhao Profile

Dr. Wei Zhao

Associate Professor at Beijing Univ of Aero & Astron

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SPIE Journal Paper | 1 January 2010

Novel image registration method using edge correlation

Xi Cai, Wei Zhao

OE, Vol. 49, Issue 01, 017003, (January 2010) https://doi.org/10.1117/12.10.1117/1.3284781

KEYWORDS: Image registration, Image fusion, Edge detection, Optical engineering, Image sensors, Wavelets, Remote sensing, Sensors, Wave sensors, Image restoration

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By considering edge similarity of images sensed in different wave bands of the same landform, a novel image registration method using edge correlation as the similarity measure is proposed. Edge correlation involves spatial information of images, which reflects well their structures. We employ significant edge curves with long length, instead of isolated edge points, to calculate the correlation which makes the proposed method more robust to noise. Experimental results prove that, for images corresponding to the same landform, edge correlation nearly always yields a single sharp peak at the correct alignment parameters, and the proposed method has more accurate registration results than mutual-information-based methods, especially under noisy conditions.

Proceedings Article | 11 June 2003 Paper

Pixel-based image fusion with false color mapping

Wei Zhao, Shiyi Mao

Proceedings Volume 4898, (2003) https://doi.org/10.1117/12.467362

KEYWORDS: Image fusion, Image filtering, Image enhancement, Image sensors, Optical filters, Sensors, Synthetic aperture radar, Composites, Visibility, Image resolution

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In this paper, we propose a pixel-based image fusion algorithm that combines the gray-level image fusion method with the false color mapping. This algorithm integrates two gray-level images presenting different sensor modalities or at different frequencies and produces a fused false-color image. The resulting image has higher information content than each of the original images. The objects in the fused color image are easy to be recognized. This algorithm has three steps: first, obtaining the fused gray-level image of two original images; second, giving the generalized high-boost filtering images between fused gray-level image and two source images respectively; third, generating the fused false-color image. We use the hybrid averaging and selection fusion method to obtain the fused gray-level image. The fused gray-level image will provide better details than two original images and reduce noise at the same time. But the fused gray-level image can't contain all detail information in two source images. At the same time, the details in gray-level image cannot be discerned as easy as in a color image. So a color fused image is necessary. In order to create color variation and enhance details in the final fusion image, we produce three generalized high-boost filtering images. These three images are displayed through red, green and blue channel respectively. A fused color image is produced finally. This method is used to fuse two SAR images acquired on the San Francisco area (California, USA). The result shows that fused false-color image enhances the visibility of certain details. The resolution of the final false-color image is the same as the resolution of the input images.

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