Editorial Feature

Bringing Clarity to the Dark: Using Sensors in Low-Light Imaging

The demand for high-quality images has driven the development of low-light imaging technology, making sensors a critical component in capturing images in challenging lighting conditions. This article explores the significance of sensors in low-light imaging and their potential for future innovations.

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Low-light imaging enables high-quality image capture in low-light conditions, where the ambient light flux is below 1 lux, such as at night or in dimly lit areas. This technology is achieved through image intensifiers, active illumination, and low-light image sensors.

Low-light technology has grown significantly in recent years, driven by increased demand for security and surveillance and the adoption of low-light cameras in the aerospace, automotive, and defense industries.

Low-light imaging faces two main challenges: low visibility, which leads to a lack of detail and clarity, and increased noise that disrupts image content. In addition, under/over-exposure issues in low-light images make it challenging for computer vision techniques developed for daytime scenes to work effectively.

How do Image Sensors Enable Low-Light Imaging?

Image sensors capture light and convert it into electrical signals that can be processed into an image on a screen. In low light conditions, image sensors are designed to increase their light sensitivity to produce a clear image.

Charge-coupled device (CCD) image sensors store the electrical charge in each photosite before transferring it to a readout device. In contrast, complementary metal-oxide semiconductor (CMOS) image sensors convert the light to charge and voltage at the photosite before sending it to a readout device.

Due to pixel arrangement, CCD sensors outperform CMOS sensors in low-light conditions. However, recent advancements in CMOS technology have resulted in global shutter CMOS sensors with comparable image quality to CCD.

Key Characteristics Enabling Low-Light Imaging in Image Sensors

Capturing images in low-light environments is critical for security, biometrics, and consumer imaging applications. Accurate differentiation of small signal variations in pixels is crucial for capturing details in low-light environments, and it significantly impacts the quality of resulting images.

It is important to evaluate various sensor characteristics that impact image quality, system complexity, and cost to select the ideal sensor for imaging systems in low-light environments. These characteristics include signal-to-noise ratio, dark current, quantum efficiency, fill factor, color sensitivity imbalance and sensitivity.

Dark Current and Quantum Efficiency

Dark current is the amount of current discharging the photodiode in the absence of incident light, and a lower value is better to reduce noise in the output. Quantum efficiency measures the proportion of incoming photons converted into electrons, and higher values are desirable for better detection of dimly lit objects.

Signal to Noise ratio (SNR)

The signal-to-noise ratio (SNR) is a critical factor in the low-light performance of an imaging sensor, as it indicates the ratio of signal to noise in an image, with a higher SNR indicating lesser noise. The SNR of a sensor is specified in decibels (dB) and is essential for capturing subtle differences in light levels in low-light conditions.

Low-light sensors are designed to minimize noise components like dark current and transistor noise to achieve a higher SNR, resulting in good-quality images.


Sensitivity or responsivity is crucial for a sensor's low light performance as it measures its efficiency in converting light into electrical signals. High sensitivity is essential for quality images in low-light conditions.

Pixel Size and Fill Ratio

The size of a pixel's active area and its fill ratio are critical factors in determining a sensor's low-light performance. Larger pixels can capture more photons, improving the dynamic range but also increasing manufacturing costs and sensor size.

Microlenses can be added to increase the effective fill ratio but at the expense of additional processing costs and design implications for the camera lens.

Color Sensitivity Imbalance

Color sensitivity imbalance is a challenge in sensor design for capturing color images in low-light conditions, as different color pixels have varying sensitivity to different frequencies of light. This imbalance can be corrected digitally but adds to analog-to-digital converters (ADC) quantization noise.

Panasonic Develops Low-Light Hyperspectral Imaging Sensor for Temperature-Sensitive Applications

Panasonic has developed a new hyperspectral imaging sensor that offers the world's highest sensitivity for low-light conditions. It uses a "compressed" sensor technology that efficiently develops images by thinning out and reconstructing data.

The sensor employs a distributed Bragg reflector (DBR) structure that transmits multiple wavelengths of light and offers a sensitivity around ten times higher than conventional technologies.

The hyperspectral images and video can be captured under indoor levels of illumination (550 lux), which significantly increases the technology's usability due to its higher frame rate.

This technology has improved low-light imaging and has various applications, such as the inspection of foods and tablets, that can now be performed without the risk of high levels of illumination raising their temperature.

Quanta Image Sensor for Highest Pixel Resolution in Ultra-Low-Light Conditions

Quanta image sensors (QIS) are becoming more popular due to their ability to address the limitations of conventional CMOS sensors with small pixels and poor signal-to-noise ratio in low-light situations. These sensors enhance low-light imaging performance by using an array of specialized small pixels capable of high-speed readout, photon-number-resolving, and high dynamic range (HDR) imaging.

A study published in Scientific Reports developed a 163-megapixel quanta image sensor that enables HDR imaging and photon-number-resolving capabilities in a single device, achieving the highest pixel resolution in ultra-low-light conditions (110mlux) among low-noise photon-number-resolving image sensors.

The QIS was fabricated using a standard CMOS process with backside illumination and2-layer wafer stacking. It demonstrated reliable photon number resolving with low read noise (0.35 e- rms) and extended full-well capacity (20k e-), resulting in superior low-light and HDR imaging performance.

The sensor is suitable for various low-light imaging applications, such as smartphones, medical diagnostics, and industrial and life science imaging.

Samsung Unveils Industry's Smallest Pixel Image Sensor

Samsung recently unveiled the ISOCELL HP3, a 200MP camera sensor with the industry's smallest 0.56-μm-pixels, that promises improved low-light imaging in smartphones.

The HP3 sensor employs the Tetra2pixel technology to combine neighboring pixels and create larger virtual pixels in low-light conditions, thereby simulating different pixel sizes to accommodate varying lighting levels.

The HP3 sensor uses Super QPD technology to enable all 200 million pixels to be used as focusing agents in low-light conditions. It analyzes pattern data from groups of four adjacent pixels to identify horizontal and vertical pattern changes, resulting in faster and more accurate auto-focusing in dimly lit environments.

The Samsung Galaxy S flagship smartphones will feature this new sensor, improving low-light photography.

Future Advancements in Low-Light Image Sensors

Low-light image sensors are increasingly important for capturing high-quality images in any lighting condition.

Researchers are investigating the potential of machine learning algorithms to enhance low-light sensor performance by training them on large datasets of low-light images to improve object detection accuracy in challenging lighting conditions.

Ongoing research in quantum efficiency, pixel size, sensor technology, and LiDAR integration will significantly enhance the quality and functionality of low-light imaging sensors, benefiting surveillance, security, medical imaging, and automobile industries.

Regional Spotlight: Image Sensors in Europe

References and Further Reading

Panasonic. (2023). Panasonic Develops Hyperspectral Imaging Technology with the World's Highest Sensitivity. [Online]. Panasonic Newsroom Global. Available at: https://news.panasonic.com/global/press/en230126-2 (Accessed on 9 May 2023)

Ma, J., Zhang, D., Robledo, D., Anzagira, L., & Masoodian, S. (2022). Ultra-high-Resolution Quanta Image Sensor with Reliable Photon-Number-Resolving and High Dynamic Range Capabilities. Scientific Reports. doi.org/10.1038/s41598-022-17952-z

Samsung. (2023). Samsung Unveils ISOCELL Image Sensor with Industry's Smallest 0.56μm Pixel. [Online]. Samsung Newsroom. Available at: https://news.samsung.com/global/samsung-unveils-isocell-image-sensor-with-industrys-smallest-0-56%CE%BCm-pixel (Accessed on 9 May 2023)

York, T., & Jain, R. (2011). Fundamentals of image sensor performance. [Online]. Washington University in St. Louis. Available at: https://classes.engineering.wustl.edu/~jain/cse567-11/ftp/imgsens/ (Accessed on 9 May 2023)

Beletkaia, E. (2021). CMOS Sensors Drive Low-Light Applications. [Online]. Photonics. Available at: https://www.photonics.com/Articles/CMOS_Sensors_Drive_Low-Light_Applications/a67465 (Accessed on 9 May 2023)

Xu, K., Yang, X., Yin, B., & Lau, R. W. (2020). Learning to Restore Low-Light Images via Decomposition-and-Enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2281-2290. https://ieeexplore.ieee.org/document/9156446

Argueta, V. (2020). Which is Better a CCD or CMOS Image Sensor? [Online]. Opticsforhire. Available at: https://www.opticsforhire.com/blog/ccd-vs-cmos-image-sensor-selection/ (Accessed on 9 May 2023)

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Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.


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