{"id":7259,"date":"2018-05-04T09:33:38","date_gmt":"2018-05-04T07:33:38","guid":{"rendered":"https:\/\/www.adimec.com\/?p=7259"},"modified":"2018-07-17T14:19:07","modified_gmt":"2018-07-17T12:19:07","slug":"implementing-a-visible-camera-for-both-daylight-and-lowlight-vision","status":"publish","type":"post","link":"https:\/\/www.adimec.com\/ja\/implementing-a-visible-camera-for-both-daylight-and-lowlight-vision\/","title":{"rendered":"Implementing a visible camera for both daylight and lowlight vision"},"content":{"rendered":"
In many outdoor and global security applications it is desired to get good image performance of a camera in both daylight and at night. In such cases the camera needs to be IR sensitive to perform better at night or needs to be sensitive to a certain wavelength. However, the infrared component is not desired in daylight as it will influence color reproduction. When designing a system, you have three choices for dealing with this contradiction in the specification.<\/p>\n
The correct IR-cut filter depends on the application and must be chosen by the system designer. Here we show the consequences on the image color when an IR-cut filter is used or when the IR-cut filter is removed.<\/p>\n
For optimal performance a camera\u2019s sensor response to light is matched with the perception of the human eye as close as possible. Figure 1 shows the spectral response of the human eye. An example of the spectral response of a color sensor can be found in Figure 2.<\/p>\n
\nFigure 1: Spectral response human eye<\/td>\n | \nFigure 2: Typical spectral response of a color CMOS image sensor<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/p>\n When comparing the two spectral responses there are clear differences. We compensate for the imperfections by post-processing the colors of the RAW sensor image with a color matrix. \u00a0The color matrix thus matches the spectral responsivity of the camera with that of the human eye.<\/p>\n When looking at the sensor performance at >700 nm a rise of the blue and green pixels in wavelength responsiveness is seen. This increase in response for IR light makes it more difficult to render good color. The IR light that is normally invisible to the human eye will contribute to the color representation. It gets even worse when looking at organic objects as these might reflect certain IR wavelengths more than inorganic objects.<\/p>\n Using an IR cut filter will make the camera blind for IR light, just like our eyes. Without detecting IR light, calculating a natural color representation from the RGB signal with the color matrix will provide a much better result.<\/p>\n A practical example<\/h2>\n
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