Abstract : An embedded image acquisition system based on a CMOS image sensor was developed. This system achieves efficient image acquisition and transmission, converting the acquired Bayer format data into RGB format. The embedded system design facilitates integration and miniaturization. Keywords : CMOS image sensor; DSP; Bayer format; image acquisition Introduction Image sensors, as fundamental devices, can acquire and convert information, extend visual functions, and provide intuitive, realistic, multi-layered, and rich visual image information. CMOS image sensors have been under development since the 1970s, but due to manufacturing limitations, they have not seen significant development due to drawbacks such as low illumination sensitivity, high noise, and low image resolution. Compared with CCD image sensors, which require external amplifiers and analog-to-digital converters, resulting in large size and limited readout speed, CMOS image sensors integrate the entire image system onto a single chip, offering advantages such as high integration, small footprint, low power consumption, and low cost. Therefore, with technological advancements, CMOS image sensors have received increasing attention and become a research hotspot. This system design utilizes a Micron MT9T001 CMOS image sensor in conjunction with a TI DM642 processor to implement an embedded image acquisition system. The system converts the acquired images using the DSP before transmitting them to a display. It offers advantages such as small size, low cost, high reliability, and easy installation. System Design This image acquisition system mainly consists of a CMOS image sensor, a DSP, and processing circuitry. The specific implementation scheme involves the CMOS image sensor being configured and receiving a start signal to begin image acquisition. The acquired image information is then sent to the DSP to prepare for subsequent image format conversion, processing, and display. The DSP stores the image data acquired by the CMOS image sensor in external SDRAM for processing. Based on the specific image recognition algorithm requirements, it processes the data and transmits the results to the display. The system block diagram is shown in Figure 1. [align=center] Figure 1 System Block Diagram[/align] CMOS Image Sensor The CMOS image sensor selected for this system is the Micron MT9T001. This is a 1/2-inch CMOS color image sensor with 3 megapixels (2048×1536). The chip can operate in its default mode, or the user can program it to customize frame size, exposure time, gain, or other parameters. In default mode, it outputs a QXGA image (2048×1536) at up to 12 frames per second and a VGA image (640×480) at up to 93 frames per second. It integrates a 10-bit analog-to-digital converter, and has dedicated output pins for the line synchronization (LINE_VALID) and frame synchronization (FRAME_VALID), which are synchronized with the valid data, just like the pixel clock (PIXCLK). The chip also integrates amplifiers, clock control circuitry (inverting and phase adjustment), image size adjustment, dot positioning, white balance adjustment, exposure adjustment, frame rate adjustment, and many other functional circuits, all controlled via a serial bus (SCLK and SDATA). This system selects a high-resolution CMOS image sensor, which can directly convert the acquired image information into digital signal output, eliminating the need for an analog-to-digital converter and effectively simplifying the system's hardware design. Furthermore, since this system is used for microscopic image acquisition, a high-resolution CMOS image sensor can produce clearer images, simplifying subsequent image processing. DSP Main Processor In this system design, an embedded system is used to improve the traditional observation method of an optical microscope. A high-speed digital signal processor is used to handle image processing and transmission, effectively reducing the instrument's size and facilitating system integration and miniaturization. For the DSP, this system selects the TI C6000 series TMS320DM642 digital multimedia processor. This is a high-performance DSP based on the C64x core, with an extended high-level very long instruction word architecture, featuring 64 32-bit general-purpose registers, 8 independent computational function units, including two multipliers and six arithmetic logic units, and can operate in parallel, thus executing multiple instructions simultaneously. The DM642 employs a two-level cache structure and a 64-channel independent EDMA controller. In addition, the DM642 has a rich set of peripheral device interfaces. During system operation, the DSP receives image signals from the CMOS image sensor via the video port and uses the external memory interface (EMIF) to store and exchange data with Flash and SDRAM. Simultaneously, using the DSP as a processor also prepares the necessary components for subsequent recognition algorithms. System Acquisition Program In this system, the single-frame image acquisition process is as follows: After system power-on, the program boots from Flash, thus completing the program import and initialization. The DSP configures the CMOS image sensor via the I2C bus. Once the CMOS image sensor starts working, it converts the acquired image information into digital signals and sends them to the DM642 processor. The image data is first stored in the FIFO of the DM642 video port. When the threshold set in the FIFO is reached, the DSP sends a signal to activate the EDMA channel, which moves the image data from the FIFO to the SDRAM. The DSP then further processes the image data in the SDRAM, sending the processed image data to another address area of ​​the SDRAM. A start signal is then sent to instruct the EDMA to transport the processed image data to the FIFO of the output video port. After encoding by the image encoding chip, the data is transmitted to the display for display. Image Format Conversion Algorithm Design This system uses a MICRON MT9T001 3-megapixel CMOS image sensor, which obtains color information through a color filter array (CFA). However, only one color component is available at each pixel location. To recover the other two missing color components, estimation must be performed using adjacent pixels; this process is called CFA interpolation. The output image format is Bayer format, where odd-numbered scan lines acquire and output R, G, R, G… respectively, and even-numbered scan lines acquire and output G, B, G, B… respectively. Its general format is shown in Figure 2. Figure 2: Bayer Format Diagram. Each pixel within the photosensitive area of ​​a CMOS image sensor corresponds to a color of light, where R senses red light, G senses green light, and B senses blue light. In this Bayer format image, half of the image pixels are allocated to the G component, while the R and B components occupy the other half. Because the G component is twice the size of the R and B components, a good interpolation method for the G component can improve not only the quality of the G component but also the quality of the R and B components. In the design of this system, the acquired image needs to be transmitted to a display for display; therefore, the acquired image information needs to be converted into an easily displayable RGB format. For the format conversion method, bilinear interpolation is used to convert the Bayer format image information output by the CMOS image sensor into RGB format. Its basic principle is to obtain the two missing color components at each pixel location by averaging the pixels with the same components in the neighborhood centered on the pixel itself. Figure 3 shows the pixel numbering in Bayer format. Based on the pixel numbering shown in Figure 3, the bilinear interpolation formula for pixels G1 and R2 is: where represents the RGB color components of point G1 after format conversion, and represents the RGB color components of point G2 after format conversion. Similarly, the format conversion calculation method for the B component is similar to that for the R component. The advantages of this method are its simplicity, ease of implementation, and low hardware resource consumption; however, it also has certain drawbacks, namely, neglecting boundary issues during color conversion. These can be improved in future algorithm designs. Experimental Results According to the system design requirements, an experimental platform was built, images were acquired using a CMOS image sensor, and the Bayer image format conversion algorithm was simulated in software. To more intuitively assess the performance of the experimental system, the images acquired by the system were compared with images captured by a digital camera. The image captured by the digital camera is shown in Figure 4. Based on the system hardware and acquisition program design, the resolution of the image acquired by the CMOS image sensor is 2048×1536, as shown in Figure 5. The image converted to RGB format using the bilinear interpolation method is shown in Figure 6. [align=center]Figure 4 Image captured by a digital camera[/align] [align=center]Figure 5 Image acquired by a CMOS image sensor[/align] [align=center]Figure 6 RGB image after format conversion[/align] Analysis and comparison of the above three images show that the MT9T001 CMOS image sensor can acquire relatively clear Bayer format images. After format conversion using bilinear interpolation, the original colors of the image can be basically restored. Conclusion CMOS image sensors are a popular multi-functional imaging device following the development of CCD image sensors. They have advantages such as high integration, low power consumption, and low cost. With the development of technology and structural improvements, the performance of CMOS image sensors will continue to improve, showing broad development prospects. Using embedded systems for image acquisition is beneficial for system miniaturization design. Converting grayscale Bayer format images to color RGB format improves image visibility and broadens the application range. References : 1. Lin Fan, Wu Suntao, Guo Donghui, CMOS image sensing technology and its research progress, Semiconductor Technology, 26(12): 40-44 2. Zhao Jili, Liu Tiegen, Li Jinshen, Design of high-speed image recognition system based on M642, Journal of Electronic Measurement and Instrumentation, 2007, 21(1): 86-89 3. Luo Biqiang, Li Bin, An improved image reconstruction algorithm and its hardware implementation, China Integrated Circuits, 2007