Pi Camera! By: Juan Medina

Image Sensors: Pi Camera! By: Juan medina Outline: ● Digital Cameras ○ History (Mariner) and connectivity/social impact: ● What is an image sensor? ○ Types ○ Color separation and Performance metric ● Pi Camera ○ purpose, price, datasheet, examples! ● Conclusions Digital Cameras: History and Social Impact On July 15 of 1965, the Mariner 4 spacecraft obtained the first flyby digital image (Figure 1: https://en.wikipedia.org/wiki/Mariner_4) of Mars with a camera system designed by the NASA Jet Propulsion Laboratory. Such system utilized a video camera tube followed by a digitizer (something somehow distant from what current digital cameras are) to record 21 images on tape that where the transmitted to Earth. By the way, such transmission took about 20 days to complete. In 1975, Steven Sasson, engineer at Kodak, build the first digital camera using a newly developed CCD (will explain later) image sensor sold Fairchild Semiconductor in 1973. Such camera was 3.6 Kg, had 0.01 megapixels, and took 23 seconds to capture the first image (Figure 2: http://giantsofscience.weebly.com/steven-sasson.html). Nowadays, digital cameras are a fundamental component of our lives. A tremendous amount of digital visual content is created every day (Figure 3: http://rocketpost.com/blog/instagram-direct-how-to-use-four-tips/). From our personal life, up to an academic and professional environment, digital cameras have changed our life dramatically; image sensors are everywhere: phones, pc, security, robots, scanners, printers, etc. What’s an image sensor? An image sensor transforms the attenuation of electromagnetic radiation as it passess through or is reflected off objects into small currents that convey the information. Image sensors have a wide variety of applications which include: digital cameras, medical imaging, night vision, thermal imaging, radar, and others. The first analogue sensors used where the video camera tubes (e.g. Mariner 4). Wikipedia’s description of their functionality is: "The cathode ray is scanned across a target which is illuminated by the scene to be broadcast. The current, then, is dependent on the brightness of the image on the target." I’ll just describe the Image Orthicon (Figure 4: https://en.wikipedia.org/wiki/Video_camera_tube) which was used in the very first televisions. Light passes through a camera lens and falls into a photocathode (photosensitive plate at negative potential) where is converted into an electron image. The electrons are then accelerated and gunned against a glass where the image is created. Video Camera Tubes were followed by semiconductor charge-coupled devices (CCD) and active pixel sensors in complementary metal-oxide-semiconductor (CMOS). Figure 5 (http://www.dpreview.com/forums/post/52351544), which I obtained from a blog post, presents the general functionality of a modern image sensor. The incoming infrared light is first filtered with an IR-Blocking Filter (a). Then a physical color filter array (CFA) controls the color light reaching each color blind sensor cell (b). The latters transforms the light into electricity which is then digitized. Lastly, millions of such cells are arranged to construct a megapixel image sensor (d). CCDs (Charged Coupled Devices) In this modern sensor (the one used by Steven Sasson), an image is projected through a lens onto the capacitor array (the photoactive region) causing each capacitor to accumulate an electric charge that is proportional to the light intensity at that location. After the image is projected into onto the array a control circuit causes each capacitor to transfer its content to the next one (Figure 6: https://www.microscopyu.com/articles/digitalimaging/ccdintro.html). Lastly, a charge amplifier is used to convert the currents into a sequence of voltages that can then be sampled and digitized. This whole process is brilliantly represented by a simple example by Nixon (Figure 7: https://www.microscopyu.com/articles/digitalimaging/ccdintro.html). A gate is opened, rain drops fall, buckets are filled. A parallel register drops the water row by row in a serial bucket array. Lastly, the content of each bucket is dropped in a “Calibrated Measuring Container” and the cycle repeats. Complementary metal–oxide–semiconductor (CMOS) On the other side, CMOS is an active pixel sensor which consist of an integrated circuit containing an array of pixel sensors. Each pixel sensor contains a photodetector and an active amplifier that is constructed with CMOS transistors. Figure 7 (http://www.digitalbolex.com/global-shutter/) presents a very instructive schematic of how CMOS and CDDs functionality differ. On the left, light is sensed by a photodiode and generates an electric charge that is stored in an electron transfer register. As explained before a control algorithm moves such charges across the vertical and horizontal registers where the signal is finally amplified and digitized. Differently, on the right we see that as soon as light arrives to the photodiodes the CMOS amplifiers maximize the signal and send it through metal wires. CDDs vs CMOS Even though there is no substantial difference in image quality, CMOS can be implemented with fewer components, use less power, and can be read faster than CCD sensors. As such, CMOS are less expensive to manufacture and therefore more common. There are some hybrid sensors that leverage the advantages of CMOS and CDDs. High count pixel cameras still use CDD. Figure 8 (http://www.digitalbolex.com/global-shutter/) present some image sensor trends. CD's are still used for high performance applications such as Professional DSC, Motion Analysis and Medical Imaging. CMOS have a wider range of applications: Automotive, Toys, Phones, Biometrics, etc. Color Separation Color separation is an important topic that I would like to cover briefly. Once you have your image sensors cells (IR filter, color filter, signal transducer) you have to arrange them in some way in order to obtain an RGB image. There are different types for color-separation mechanisms such as Bayer Filter sensor, Foveon X3 sensor and 3CCD. In this blog post I’ll just cover the Bayer filter color filter array infrastructure. Figure 9 (https://en.wikipedia.org/wiki/Bayer_filter) presents the Bayer filter CFA pattern. This filter pattern is 50% green, 25% red and 25% blue, that’s why it’s also called RGBG. This proportion is used to mimic the physiology of the human eyes. Demosaicing is the process of translating the pattern into a 3 level matrix. For each pixel there has to be a value for Red, Blue and Green. There are different techniques for demosaicing. For instance, at a green pixel there are always 2 red neighbor pixels. The value of this pixels can be interpolated therefore interpolated. Performance Metrics Image sensors are commonly compared using 3 particular metrics: 1. pixel count: total number of pixels (NxW); often measured in megapixels. Pixel counts is an important metric, however, image sensors with the same number of pixels but with different size can result in different quality. Larger sensors produces images with better resolution. 2. lens quality: resolution, distortion, dispersion. 3. dynamic range: the range of luminosity that can be reproduced accurately. Pi Camera The Pi Camera is a Raspberry Pi 5 megapixels camera module capable to record 1080p video and still images (Figure 10: http://www.adafruit.com/images/1200x900/1367-00.jpg). The Raspberry Pi (Rpi) is some sort of Arduino's next level. It's like a small computer running on linux that has digital inputs and outputs. Differently from the Arduino, the Rpi does not have analog I/O. The Pi Camera module connects directly to the Rpi Camera Serial Interface using a ribbon cable. The board itself is very small (25 x 20 x 9 mm) and weighs about 3 g. Differently from more sophisticated cameras, the module has a fixed focus lens onboard (although this focus can be carefully changed). The Rpi combined with the camera module has several applications: from rapid prototyping of Internet of Things (IOTs) devices up to time-lapse devices. For instance, I've been using this sensor to build an smart garbage can. The e-can is a regular trash container equipped with the Rpi and the Pi Camera. The goal is to take pictures of garbage and try to classify them in real-time (e.g. plastic vs. metal). The Rpi is quite cheap: it costs about 35 USD (you can buy it here: https://www.sparkfun.com/products/11868). Something very important to understand is that the Pi Camera is an integrated circuit designed to be smoothly connected to the Rpi. However, the particular image sensor used in the Pi Camera is a 1/4" color CMOS QSXGA image sensor manufactured by OmniVision. The datasheet (http://cdn.sparkfun.com/datasheets/Dev/RaspberryPi/ov5647_full.pdf) its a very large pdf that describes the sensor thoroughly. In this blog post, I'll review the general aspects of this datasheet. First I'll start with the sensor features (Figure 11). Even though there is a long list of features, I'll comment just on 3: 1. 1.4x1.4 um pixel. The datasheet indicates that the sensors a OmniBSI technology for high performance (high sensitivity, low noise). 2. It has automatic image control functions like: Automatic Exposure Control (AEC), Automatic White Balance (AWB) and others. 3. Support for output formats: 8-10 bit RGB data. As we learned previously in this blog, CMOS final output is a serial analog signal that can be digitized. In this particular case, the output of the sensor is the digitized data. Another important information presented in the datasheet are the key specifications of the sensor (Figure 12). From this list we can learn that there are 2592x1944 active sensors cells (5 megapixels). We see that the core of the sensor works with 1.5V and it has an embedded voltage regulator, while the analog electronics work with 3V and digital I/O uses 1.7-3V.

Load more