视觉诱导晕动症（VIMS）是虚拟现实（VR）体验过程中可能产生的一种常见症状，该症状严重降低了用户的体验的舒适度，阻碍了VR技术的推广和发展。虚拟空间畸变是影响VR体验舒适度的潜在因素之一，近眼显示设备不完全的畸变校正会产生虚拟空间畸变，进而可能引发VIMS。针对虚拟空间畸变对VIMS的影响程度，本文模拟VR技术的体验过程，对以VR技术为代表的近眼显示技术畸变机理进行分析，设计了3个不同畸变参数下VIMS等级评价的视觉感知实验，并对实验的主客观数据进行统计分析。结果表明，虚拟空间畸变对VIMS症状具有显著性影响，受测者观看立体影像的畸变程度越大，所引起的VIMS症状越明显。该研究可为VR技术体验的舒适度提升方向提供理论依据。

为了克服传统方法的一些缺陷和单一特征提取信息的不足，在进一步提高红外和可见图像融合的同时，寻找针对不同类型特点的适应能力强的方法，提出了一种基于多判断与加权最小二乘优化（WLS）的非下采样轮廓波变换（NSCT）红外可见图像融合方法。首先，采用NSCT对图像进行多尺度分解，得到图像的低频和高频子带。其次，低频子带选择局部平方熵和修正拉普拉斯和（SML）来相互补充，在保证好的对比度下提取少量细节信息；高频子带充分考虑底层特征的重要性，选择相位一致性（PC）、局部加权修正拉普拉斯算子和（WSML）以及局部加权能量（WLE）相互补充的方式融合细节层，对其进行WLS优化，融合后的图像细节更自然，更适合人眼视觉感知。最后，对融合后的低频和高频子带进行逆变换，得到融合图像。对不同类型特点的图像进行了实验验证，实验结果表明，与其他融合方法相比，本文方法在主观上目标显著、背景清晰、视觉效果好。在4个客观评价指标平均梯度（AG）、信息熵（IE）、空间频率（SF）、互信息（MI）中，在保证MI指标比较好的前提下，其他3个指标都处于最好的状态，尤其是对于光照均匀的camp图像，AG和SF与最好的数值相比提高了6.9%和4.8%，从而验证了本文方法的有效性。

The human visual system, dependent on retinal cells, can be regarded as a complex combination of optical system and nervous system. Artificial retinal system could mimic the sensing and processing function of human eyes. Optically stimulated synaptic devices could serve as the building blocks for artificial retinas and subsequent information transmission system to brain. Herein, photonic synaptic transistors based on polycrystalline MoS₂, which could simulate human visual perception and brain storage, are presented. Moreover, the photodetection range from visible light to near-infrared light of MoS₂ multilayer could extend human eyes’ vision limitation to near-infrared light. Additionally, the photonic synaptic transistor shows an ultrafast speed within 5 μs and ultralow power consumption under optical stimuli about 40 aJ, several orders of magnitude lower than biological synapses (50 ms and 10 fJ). Furthermore, the backgate control could act as emotional modulation of the artificial brain to enhance or suppress memory function, i.e. the intensity of photoresponse. The proposed carrier trapping/detrapping as the main working mechanism is presented for the device. In addition, synaptic functionalities including short synaptic plasticity, long synaptic plasticity and paired-pulse facilitation could be successfully simulated based on the prepared device. Furthermore, the large difference between short synaptic plasticity and long synaptic plasticity reveals the better image pre-processing function of the prepared photonic synapses. The classical Pavlovian conditioning associated with the associative learning is successfully implemented as well. Therefore, the efficient and rich functionalities demonstrate the potential of the MoS₂ synaptic device that integrates sensing-memory-preprocessing capabilities for realizing artificial neural networks with different emotions that mimic human retina and brain.The human visual system, dependent on retinal cells, can be regarded as a complex combination of optical system and nervous system. Artificial retinal system could mimic the sensing and processing function of human eyes. Optically stimulated synaptic devices could serve as the building blocks for artificial retinas and subsequent information transmission system to brain. Herein, photonic synaptic transistors based on polycrystalline MoS₂, which could simulate human visual perception and brain storage, are presented. Moreover, the photodetection range from visible light to near-infrared light of MoS₂ multilayer could extend human eyes’ vision limitation to near-infrared light. Additionally, the photonic synaptic transistor shows an ultrafast speed within 5 μs and ultralow power consumption under optical stimuli about 40 aJ, several orders of magnitude lower than biological synapses (50 ms and 10 fJ). Furthermore, the backgate control could act as emotional modulation of the artificial brain to enhance or suppress memory function, i.e. the intensity of photoresponse. The proposed carrier trapping/detrapping as the main working mechanism is presented for the device. In addition, synaptic functionalities including short synaptic plasticity, long synaptic plasticity and paired-pulse facilitation could be successfully simulated based on the prepared device. Furthermore, the large difference between short synaptic plasticity and long synaptic plasticity reveals the better image pre-processing function of the prepared photonic synapses. The classical Pavlovian conditioning associated with the associative learning is successfully implemented as well. Therefore, the efficient and rich functionalities demonstrate the potential of the MoS₂ synaptic device that integrates sensing-memory-preprocessing capabilities for realizing artificial neural networks with different emotions that mimic human retina and brain.

图像引擎通过多种特定的算法对图像信号进行优化，在显示系统中具有极其重要的作用和地位。图像引擎中的传统色彩优化算法由多种图像算法组合而成，存在无法进行自适应优化、容易放大图像噪声等问题。为此，提出了一种基于空洞卷积的全卷积神经网络用于构建优化算法，侧重于从人眼主观感知的角度优化图像色彩。同时，有针对性地构建大规模数据集以提高算法的泛化能力，防止过拟合。测试结果表明，所提出的算法可以有效地增强原始图像的色彩，相较于传统方法，平均峰值信噪比提升了4.01 dB，平均结构相似性提升了0.04。主观对比实验结果表明，提出的算法对图像主观感知质量有显著性影响，平均提升了61%。