The liquid crystal modulator devices (LCMD) have become an important technique in the field of hyperspectral imaging. However, the spectral resolution and accuracy of LCMD-based imaging spectrometers are limited due to their principle. To break this limitation and promote the application of LCMD, we propose a spectral reconstruction method using model-based neural networks. The calibrated spectral transmittance of LCMD and a carefully designed loss function are used to constraint the calculation. Experiments on reconstructing both substance spectra and spectral image cubes have validated the effectiveness and superiority of the proposed method.
Quality assurance of banknote printing plates is an important issue for the corporation which produces them. Every plate must be checked carefully and entirely before it's sent to the banknote printing factory. Previously the work is done by specific workers, usually with the help of powder and magnifiers, and often lasts for 3 to 4 hours for a 5*7 plate with the size of about 650*500 square millimeters. Now we have developed an automatic inspecting system to replace human work. The system mainly includes a stable platform, an electrical subsystem and an inspecting subsystem. A microscope held by the crossbeam can move around in the x-y-z space over the platform. A digital camera combined with the microscope captures gray digital images of the plate. The size of each digital image is 2672*4008, and each pixel corresponds to about 2.9*2.9 square microns area of the plate. The plate is inspected by each unit, and corresponding images are captured at the same relative position. Thousands of images are captured for one plate (for example, 4200 (120*5*7) for a 5*7 plate). The inspecting model images are generated from images of qualified plates, and then used to inspect indeterminate plates. The system costs about 64 minutes to inspect a plate, and identifies obvious defects.
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