What is Signal-to-Noise Ratio？How does it effect Embedded Vision？

I don't know if you have ever understood the concept of signal-to-noise ratio (SNR)? Those who have been exposed to embedded vision systems should know that these systems rely on advanced cameras and sensors to capture and process image and video data and provide real-time insights and responses, making them popular in industries such as healthcare and security. The signal-to-noise ratio is a key factor that can affect the visual accuracy, reliability and performance of these systems.

Maybe you are still puzzled about signal-to-noise ratio. Although you've heard of it, you don't understand what it means, how it's calculated, and why it's important. Then in this article, we will come to know more about its importance in embedded vision (e.g., smart surveillance cameras, automatic carry-over photography, etc.).

What is Signal-to-Noise Ratio?

what is sn ratio?Signal-to-Noise Ratio, or SNR for short, is a quantitative measure of the strength of a desired signal relative to the background noise (unwanted signal).snr is important for comparing useful signals with interfering signals in a system, distinguishing between various output signals, and realizing efficient output.

Signal-to-noise ratio is usually expressed in decibels (dB).The higher the value of signal to noise ratio, the better the output. In embedded vision, the signal is the data captured by the device, which may contain information that the system needs to process. Noise can be any external factor such as electromagnetic interference, vibration, etc. The lesser the effect of noise on the signal, the higher the SNR, the more useful information is in the signal, thus improving the quality and reliability of the data. For example, 90dB is better than 50dB.

so how to calculate snr?calculation of signal to noise ratio(SNR) can be use the formula and the result is expressed using decibels:

s/n ratio formula:  SNR = 20 * log10 (Signal Amplitude / Noise Amplitude)

Where Signal Amplitude is the intensity of the image or video data and Noise Amplitude is the intensity of the noise affecting the data.

Why is signal-to-noise ratio important in embedded vision?

Signal-to-noise ratio is important because it directly affects the quality of image and video data and the accuracy and reliability of analysis results. When it comes to embedded vision applications such as edge processing, such as head counting and object recognition, a high SNR is beneficial in reducing noise particles in the image and providing clearer results. And in algorithms such as machine learning and artificial intelligence, high SNR can effectively improve the accuracy of data processing and reduce errors. Meanwhile, for low-light camera modules, it can clearly reflect the impact of noise on image quality.

Impact of noise on embedded vision data

Noise refers broadly to unwanted signals that appear in image or video data, such as distortion, quantum noise, pixelation, etc., which can lead to errors in the data. The presence of these noises reduces the visualization of the data and makes it more difficult for the system to extract and process useful information from it. It also increases the size and bandwidth requirements of the data.What is noise in embedded vision?

Impact of Signal to Noise Ratio on Embedded Vision System Performance

Noise level: A low SNR amplifies the noise level, making it more difficult for the system to extract useful information from the information.
Dynamic Range: The level of SNR directly affects the dynamic range of the system, which is the ratio between the brightest to the darkest portion. A low SNR will make it more difficult for the system to distinguish between different brightnesses and contrasts.
Resolution and Sharpness: Low SNR will make object recognition become stuck which, while high SNR helps to improve the resolution and sharpness of the image, making the details more obvious and helping edge detection algorithms.

What is the relationship between SNR and camera characteristics?

SNR does not affect visualization alone, it is closely related to many characteristics of the camera. Understanding how these characteristics affect SNR can lead to better visual results.

Dynamic Range: A good dynamic range can capture more color tones, which is good for getting better SNR at different brightness levels, and better distinguishing details in light and dark areas.

ISO Sensitivity: High ISO amplifies the signal while amplifying the noise, lowering the SNR. low ISO gives better sound to noise ratio, but requires better light for exposure.

Shutter speed: faster shutter speeds reduce motion blur, but require a larger aperture or ISO, which affects SNR. slower shutter speeds in low light result in lower SNR due to increased exposure.

Sensor size: the larger the sensor the larger the pixels are, the more photons are collected and more light can be captured for a better signal-to-noise ratio. On the contrary, small pixels may generate noise and affect the SNR.

Image processing algorithms: Advanced image processing algorithms can reduce unwanted noise and improve SNR while maintaining image detail.

Aperture size: The larger the aperture, the more light there is, helping to improve the snr ratio. The smaller the aperture, the longer the exposure time required, which introduces more noise.

Why does exposure time affect SNR?

Exposure time is also a key factor in SNR, determining how long the sensor receives light. Longer exposure times can increase the number of photons captured, theoretically increasing the signal strength and improving the signal-to-noise ratio. this can also lead to the creation of more photonic and electronic noise, especially at high temperatures or during long exposures, which can degrade the image quality.

From the above we can conclude that the signal (s) is proportional to the number of photons collected during the exposure time, the latter being calculated as the product of the light intensity (I) and the exposure time (t):

When considering incident photon intensity, photon scattering noise (photon scattering noise is a type of noise inherent in any system that counts light in discrete units (i.e., photons)) also appears. The signal-to-noise ratio due to photon scattering noise (SNR_Shot) is given by the following equation:

When the exposure time is longer, the number of photons collected (N) also increases, and so does the signal (S). The square root of the signal (√S) also increases. This means that in the case of scattered grain noise, the sound to noise ratio increases with the square root of the exposure time.

Some relevant suggestions to improve SNR in embedded vision

From the above I can tell that reducing the noise or improving the signal quality can be effective in improving the SNR. for this we can come up with the following relevant optimization suggestions:

• for signal strength optimization. But avoid over-optimization to prevent amplifying the noise, resulting in no substantial improvement in the image.
• Optimize the architecture of the camera when purchasing or customizing the camera. Using a good architectural design allows for better imaging performance.
• Use a high quality sensor. High-quality image sensors with low readout noise can reduce noise and improve SNR.
• Effective thermal design lowers sensor temperature and reduces other forms of noise such as thermal noise.
• optimizes camera settings such as exposure time and shutter speed to reduce noise while capturing the best images.

To summarize

Signal-to-noise ratio is an important factor affecting embedded vision systems, which directly affects the quality of image and video data and the accuracy and reliability of analysis results. We hope that through this article we can better understand the meaning of signal-to-noise ratio, the factors affecting it, and how to improve it so that we can optimize our embedded vision applications and achieve better results.

If you need help or customize a low noise camera and integrate it into your embedded vision application, please feel free to contact us.