High frame rate and high grayscale are the main direction of development of the silicon-based micro-display. Therefore, based on the research of image dithering algorithm and multi-frame fusion scanning strategy, a high frame rate scanning strategy is proposed. Under the conditions of frame rate of 200Hz, data transmission bit width of 8 bits, grayscale of 256, and resolution of 1024×768, this strategy can make data transmission efficiency reach 100% and data transmission frequency reach 52.429MHz, which can support high frame rate display with limited bandwidth. Then use diffierent scanning strategies to process images. By the analysis of images, grayscale distribution histogram and grayscale test images, it shows that after the procession of diffierent scanning strategies, the grayscale linearity of images is good, the phenomenon of image distortion is not obvious. It proves that the strategy can meet the needs of silicon-based micro-display.
Address-event-based Dynamic Vision Sensor(DVS) and Convolutional Neural Network(CNN) have been widely researched in recent years. However, the collected data of DVS are easily affected by some noise, which makes it difficult to identify the target during the classification processing. In order to solve the problem of misclassification, a novel improved CNN(NI-CNN) technique is proposed in this paper. Firstly, the appropriate number of event pulses are chosen and mapped to the frame domain, then the optimization denosing approach is utilized to the whole classification system. Secondly, reducing intra-class spacing and enlarging inter-class divergence by joint loss function which is adjusted regularization parameters. Numerical comparisons between our proposed approach and some state-of-the-art solvers, on several accessible databases, are presented to demonstrate its efficiency and effectiveness.
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