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Unlike conventional methods, which generally work on a color image processed by the image processing module (IPM), the proposed method works directly on the Bayer raw image.
These approaches also utilize RGB images processed by the image processing module (IPM).
However, since the conventional weighting functions are designed to adapt to the RGB images processed by the image processing module such as the gamma correction, they are improper for the BP-LDR images.
The image is further processed in image processing module in mat lab for the texture feature extraction namely the curvelet transforms data.
This layer constitutes the following modules: database management module, image processing module, and routing decision module.
Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image.
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Thus, many image processing modules run concurrently on several PCs.
The decision module is invoked by sonar-sleeping, sonar-sensing, time synchronization, and image processing modules.
The fast and convenient processing opens news possibilities, for example, configurable image processing modules in order to massive processing of images.
Unlike the aforementioned methods, the proposed method does not rely on other image processing modules for pre- or post-processing, which avoids the possible vulnerability of these techniques when processing images of complex structures.
Different from most existing methods, the proposed method does not rely on other image processing modules for pre- or post-processing, which avoids the likely vulnerability of these techniques when processing images with complex structures.
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