{"id":8070,"date":"2018-07-12T15:40:14","date_gmt":"2018-07-12T13:40:14","guid":{"rendered":"https:\/\/www.adimec.com\/?p=8070"},"modified":"2023-07-11T17:38:16","modified_gmt":"2023-07-11T15:38:16","slug":"upgrade-to-cmos-cameras-without-changing-optics","status":"publish","type":"post","link":"https:\/\/www.adimec.com\/ja\/upgrade-to-cmos-cameras-without-changing-optics\/","title":{"rendered":"Upgrade to CMOS cameras without changing optics"},"content":{"rendered":"

Adimec Adaptive Resolution Introduction<\/h2>\n

For many applications the optical format, often driven by an initial choice of an existing (legacy) image sensor, is a given.\u00a0 These legacy systems tended to be CCDs, many of which are going the way of the cassette tape. Upgrading cameras can also mean a costly upgrade in optics. Trends in CMOS imagers are to lower read noise, increase pixel throughput and add more and smaller pixels. This opens the possibility to mimic an optical format by digital image down-scaling, i.e. Adaptive Resolution. With Adaptive Resolution, the improved performance of CMOS cameras can be utilized without changing the optics.<\/p>\n

Image scaling<\/h3>\n

Image scaling involves a 2D re-sampling of the intrinsic sensor image. Physical pixels are digitally converted to virtual pixels of a different size.<\/p>\n

Adaptive Resolution Feature<\/h3>\n

Here we provide a basic introduction to the Adaptive Resolution (AR) feature of Adimec cameras. It will also describe the situations where AR can be applied.<\/p>\n

Adaptive Resolution again is an image scaling feature, which rescales the images from the sensor to a new desired image output format. In order to remove confusion between the various options that influence camera resolution we will show them side by side.\u00a0 Specifically, we consider: No Scaling, Region of Interest (ROI), Zoom, Binning and Adaptive Resolution.<\/p>\n

\"Adaptive<\/p>\n

 <\/p>\n

\"Adaptive<\/p>\n

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Scaling features off is the baseline condition of the sensor and thus has no relative advantages or disadvantages. ROI can provide for faster frame speed but you lose the full sensor FoV.\u00a0 Zoom allows small regions to be enlarged to better investigate an area.\u00a0 Depending on the implementation and whether the interface or sensor is the limiting factor, binning can increase frame rate, increase full well, as well as reduce noise due to averaging.\u00a0 The key disadvantage to binning is that you can only compress or combine whole pixels (e.g. 2×2, 4×4).<\/p>\n

Then we have Adaptive Resolution.\u00a0 The advantage of AR is that you can keep the legacy optics in your system while profiting from the noise improvements of CMOS over CCD.\u00a0 You can rescale by a ratio, allowing resizing to any format. AR calculates the partial impact of the applicable sensor pixels to get the correct output pixel. Due to ratio scaling, pixel size simulation is possible. (Example: rescale 3,45×3,45 um pixels to behave as 5,5×5,5 um pixels.)\u00a0 Due to ratio scaling, full sensor FoV can be rescaled to desired Image output (Example 4096×3072 => 1920×1080). The disadvantage is that this is only digital, and that image deformation is possible: but you can scale X and Y in same factor to prevent deformation.\u00a0 This may still be a bit confusing so let\u2019s consider the following four examples.<\/p>\n

Practical use cases for Adaptive Resolution<\/h3>\n

1. Rescale the image size to application desired image output format<\/strong><\/p>\n

To display an image on a monitor\/tv the image does not need to contain more pixels than the display resolution, the best approach is to provide the display with an image that has exactly the same resolution. With AR you can reshape the image size to match your display, without losing the sensor FoV.<\/p>\n

*NOTE: It\u2019s possible to display a square sensor to an HD (rectangular) display, but then the image will be deformed however you can do an ROI on the sensor as well as AR to get an HD rectangular display.<\/p>\n

Example:<\/em><\/p>\n

Adimec TMX50: sensor 2464×2056 pixels<\/em><\/p>\n

Sensor FoV on a HD display: 2464×2056 => 1920×1080<\/em><\/p>\n

Compress ratio 1.283(X) : 1.904 (Y) => note that image will be deformed<\/em><\/p>\n

Largest sensor ROI without deformation: 2464×1386<\/em><\/p>\n

Compress ratio 1.283(X) : 1.283(Y)<\/em><\/p>\n

2. <\/em>Replace old (or discontinued) sensors<\/strong><\/p>\n

Most camera systems are built to specification, with an original camera designed in. When this camera sensor is discontinued by the manufacturer or there appears a new (better) camera sensor on the market, the designed camera system needs a lot of redesigns to be able to work with the new camera sensor.<\/p>\n

Usually the system has a dedicated lens system and software build for the old sensor FoV, and pixel size. If a replacement sensor has another pixel format and\/or new sensor size, then the lens assembly and\/or software must be changed, which usually prohibits a change due to the costs involved.<\/p>\n

With Adimec AR the lens assembly and\/or interface firmware do not need to be changed (as long as the new sensor is equal or bigger)<\/p>\n

Example:<\/em><\/p>\n

A system is designed with a sensor: 1920×1080 resolution and 5.5×5.5 um pixels => output image 1920×1080<\/em><\/p>\n

Replaced with:<\/em><\/p>\n

TMX55 (4096×2160) 3.45×3.45 um pixels -> output image 1920×1080 with same FoV:<\/em><\/p>\n

5.5\/3.45 = 1.666 compression ratio<\/em><\/p>\n

Sensor ROI = (1920*1.666)x(1080×1.666) = 3200×1800 => Image output resolution 1920×1080.<\/em><\/p>\n

3. Create a virtual pixel format, with better performance compared to the original sized pixel<\/strong><\/p>\n

Adaptive resolution calculates the bigger virtual pixels out of the original smaller sensor pixels. This is a cumulative calculation, like with binning, and because of the calculation the virtual pixel has additional benefits:<\/p>\n