To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.展开更多
It has long been a challenging task to improve the light collection efficiency of conventional image sensors built with color filters that inevitably cause the energy loss of out-of-band photons. Here, we demonstrate ...It has long been a challenging task to improve the light collection efficiency of conventional image sensors built with color filters that inevitably cause the energy loss of out-of-band photons. Here, we demonstrate a pixelated spectral router based on a sparse meta-atom array, which can efficiently separate incident R(600±700 nm), G(500±600 nm), and B(400±500 nm)band light to the corresponding pixels of a Bayer image sensor, providing over 56% signal enhancement above the traditional color filter scheme. It is enabled by simple compound Si_(3)N_(4) nanostructures, which are very suitable for massive production. Imaging experiments are conducted to verify the router's potential for real applications. The complementary metal-oxide-semiconductor(CMOS)-compatible spectral router scheme is also found to be robust and can be freely adapted to image sensors of various pixel sizes, having great potential in building the new generation of high-performance image sensing components.展开更多
文摘To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
基金supported by the Natural Science Foundation of China (Nos. 62075196 and 62105282)the Natural Science Foundation of Zhejiang Province (No. LDT23F05014F05)+1 种基金the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (No. 2021R01001)the Fundamental Research Funds for the Central Universities。
文摘It has long been a challenging task to improve the light collection efficiency of conventional image sensors built with color filters that inevitably cause the energy loss of out-of-band photons. Here, we demonstrate a pixelated spectral router based on a sparse meta-atom array, which can efficiently separate incident R(600±700 nm), G(500±600 nm), and B(400±500 nm)band light to the corresponding pixels of a Bayer image sensor, providing over 56% signal enhancement above the traditional color filter scheme. It is enabled by simple compound Si_(3)N_(4) nanostructures, which are very suitable for massive production. Imaging experiments are conducted to verify the router's potential for real applications. The complementary metal-oxide-semiconductor(CMOS)-compatible spectral router scheme is also found to be robust and can be freely adapted to image sensors of various pixel sizes, having great potential in building the new generation of high-performance image sensing components.
