A device has been designed for off-line optical paper inspection and quality control of stripes and holes in the cigarette paper. Hardware description is first presented including main paper characteristics to be measured. Typical paper stripe and holes structures are then discussed with image processing and analysis considerations to discriminate these structures, focusing in the problems derived from the small area of holes and of their internal structure that is analyzed with a confocal microscope. Algorithms for image processing and analysis are described. These algorithms involve equalization, binarization, stripes structure detection, holes distribution and statistics.
Ultrasound images, as a special case of coherent images, are normally corrupted with multiplicative noise i.e. speckle noise. Speckle noise reduction is a difficult task due to its multiplicative nature, but good statistical models of speckle formation are useful to design adaptive speckle reduction filters. In this article a new statistical model, emerging from the Multiplicative Model framework, is presented and compared to previous models (Rayleigh, Rice and K laws). It is shown that the proposed model gives the best performance when modeling the statistics of ultrasound images. Finally, the parameters of the model can be used to quantify the extent of speckle formation; this quantification is applied to adaptive speckle reduction filter design. The effectiveness of the filter is demonstrated on typical in-vivo log-compressed B-scan images obtained by a clinical ultrasound system.
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