Performance evaluation of high sensitive DRE camera for cultural heritage in subdued light conditions

Sanzo Ugawa, Takeo Azuma, T. Imagawa, Yusuke Okada
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引用次数: 2

Abstract

We proposed a color video generation method for spatio-temporal high resolution video imaging in dark conditions.[1] The method (dual resolutions and exposures(DRE) method) consists of a high sensitive imaging with employing long time exposure and a subsequent spatio-temporal decomposition process which suppresses a motion blur caused by the long time exposure. Imaging step captures RGB color video sequences with different spatio-temporal resolution sets to increase light amount. Processing step reconstructs a high spatio-temporal resolution color video from those input video sequences using the regularization framework. The performance of DRE is regard to the spectral distribution of a subject was evaluated in this study. Firstly, the spectral distribution of a green subject (which is though to be unfavorable from the viewpoint of imaging with DRE) was measured. Secondly, this subject was captured on video with DRE, the peak signal-to-noise ratio (PSNR) of the video images was evaluated. Experimental results showed that the DRE method is effective in regard to green subjects. As for subjects that are commonly seen, their spectral distribution showed a broad shape, and the reconstructed images with DRE had high PSNRs. Moreover, even for the subject with a peaky spectral distribution (such as an LED), PSNR value was high. This is because the spectral characteristic of the color filter used with DRE has a wide crosstalk region across the colors
弱光条件下文物高灵敏度DRE相机性能评价
提出了一种用于黑暗条件下时空高分辨率视频成像的彩色视频生成方法该方法(双分辨率和曝光法)由采用长时间曝光的高灵敏度成像和随后的时空分解过程组成,该过程抑制了长时间曝光引起的运动模糊。成像步骤捕捉不同时空分辨率设置的RGB彩色视频序列,以增加光量。处理步骤利用正则化框架将这些输入视频序列重构成高时空分辨率的彩色视频。在本研究中,DRE的性能是根据受试者的光谱分布来评估的。首先,测量了绿色物体(从DRE成像的角度来看,这是不利的)的光谱分布。其次,用DRE对受试者进行视频拍摄,对视频图像的峰值信噪比(PSNR)进行评价。实验结果表明,DRE方法对于绿色物体是有效的。对于常见的被试,其光谱分布呈较宽的形状,DRE重建图像具有较高的psnr。而且,即使是光谱分布呈峰值的对象(如LED), PSNR值也很高。这是因为与DRE一起使用的彩色滤光片的光谱特性具有跨颜色的宽串扰区域
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