What is the basic concept of pixel merging and how it works? What are the advantages？

Image clarity and sensitivity have always been a common goal for users and developers alike. In recent years, the technique of “pixel binning” has become a hot topic, which improves the sensitivity of cameras with small pixel sizes in a unique way so that high-quality images can be captured in low-light conditions. While the term pixel merging is more commonly used in smartphone cameras, it is also starting to find applications in machine vision and embedded vision.

In this article we will take an in-depth look at how pixel binning works and how it can simulate larger pixel sizes by merging pixels, thus increasing the sensitivity of the camera without increasing the size of the sensor.

What role do pixels play in embedded vision and cameras?

Before exploring pixel binning technology, we must first understand the role pixels play in embedded vision and cameras. Pixels, also known as photoreceptors, are physical points on a camera's sensor that are responsible for capturing light to form an image. The size of a pixel is usually measured in microns (one millionth of a micron), and pixels smaller than one micron are considered very small.

Larger pixels can collect more light than smaller ones, which is especially important in environments where light is scarce. Therefore, in order to obtain the desired image quality in these conditions, we usually tend to choose sensors with larger pixel sizes. However, smaller pixels also have their advantages in that they are able to capture smaller objects and details. For example, Sinoseen's SNS-USB2160-v1.0-a 2mp compact small size USB camera-has a pixel size of 1.4 microns, which is considered smaller, while the D694P1-A2-E-a 2mp HDR USB camera-has a pixel size of 3 microns, which is considered larger.

This is when the problem arises. If you want a high resolution camera, its smaller pixel size, limits the sensitivity of the camera. And if you choose a camera with a larger pixel size, the sensor size increases accordingly. If your application requires both the benefits of a small pixel size camera, such as capturing tiny objects while still getting good sensitivity, traditional methods are not enough.

This is where pixel binning comes into play. Image binning cameras are able to simulate larger pixel sizes without opting for a larger sensor. In the next sections we will look at this concept in more detail.

Definition of Image Merging

what is binning meaning？Pixel binning is an innovative image processing technique that effectively increases the size of sensor pixels by combining the electrical signals of neighboring pixels, thus providing enhanced sensitivity for small pixel size cameras.
At its core, pixel binning technology allows cameras to emulate larger pixel sizes by combining multiple pixels without sacrificing resolution. This technique is an ideal solution for camera applications that seek to increase sensitivity while maintaining a miniaturized design.

How Pixel Binning Works

Pixel binning technology is implemented at the image signal processor level through the method of demosaicing, which combines the information from four neighboring pixels into a single pixel. The process involves combining a grid of 2×2, 2×1, 3×3 or 4×4 pixels into a larger “superpixel”.

During the pixel merging process, the information of each pixel is integrated into a single large pixel. This means that in the case of a 4-to-1 or 2×2 pixel merge, for example, the effective resolution of the image will be reduced to 1/4 of the sensor's resolution; however, for most embedded vision applications, this resolution tradeoff is acceptable, as a certain amount of resolution can often be sacrificed for better image quality when capturing images in low-light environments.Find out how many pixels a photo needs.

The key to pixel merging is how effectively it utilizes existing sensor designs. By combining data from neighboring pixels, the camera is able to improve its light-sensitive performance without increasing the physical size of the sensor. This technique is particularly valuable for applications that require high sensitivity in a compact space, such as in mobile devices or miniaturized industrial cameras.

In addition, pixel merging provides flexibility by allowing developers to choose different merge types (e.g., 2×2, 3×3, 4×4, etc.) to find the most suitable combination of resolution and pixel size for specific application requirements. This flexibility makes pixel merging ideal for implementing customized camera solutions.

Benefits of using pixel binning in embedded vision applications

Pixel binning technology offers several significant advantages that make it a powerful tool for improving the performance of small pixel size camera modules.

• Higher Sensitivity: By merging pixels, the sensitivity of the camera is greatly improved, which is especially useful in night vision applications and low-light environments. Larger pixels capture more light, resulting in high quality images even in low light conditions.
• Flexibility and customization: Different pixel merge types (e.g., 2×2, 3×3, 4×4, etc.) provide flexibility, allowing developers to choose the most appropriate combination of resolution and pixel size for the needs of a particular application. This is a huge advantage for developers who need to customize their camera solutions to fit specific application environments.
• Miniaturized Designs: As embedded systems become smaller and smaller, cameras with pixel merging capabilities can help limit the size of the camera while achieving the desired sensitivity. This gives product developers more room to accommodate other hardware components in the device, resulting in a more compact design.
• Suitable for specific applications: While pixel merging may not be enough to justify the resolution trade-off in bright light, in applications where increased sensitivity is required, such as security surveillance, astrophotography, or biomedical imaging, pixel merging provides a significant performance boost.
• COST EFFICIENCY: Pixel merging allows higher performance to be achieved using existing sensor technology without the need to invest in more expensive, larger sensors. This makes it a cost-effective solution, especially for projects with limited budgets.

In summary, while pixel consolidation may not be suitable for all camera applications, it offers significant performance benefits in applications that need to operate in low-light conditions. Pixel merging is an ideal choice for camera applications that seek to increase sensitivity while maintaining a miniaturized design.

Conclusion

In conclusion, pixel binning is not only an effective means of solving low-light performance problems, but also an important factor in the advancement of embedded vision technology. As the technology continues to advance, we can expect to see more innovative applications based on pixel binning in the future to further improve image quality and user experience.

Sinoseen, as a manufacturer of embedded camera modules, has more than 14 years of experience in the field, with products that include features such as high resolution, color filter-less arrays, and small pixel sizes. Ideal for applications such as digital microscopy, automatic license plate recognition, and quality inspection.

If you have a need for a custom USB 3.0 Camera Module, GMSL Camera or MIPI Camera Module to integrate into your product, feel free to contact us or visit our product page.