Taming the darkness

Taming the darkness

In the realm of artificial intelligence, the quest to replicate and augment human abilities knows no bounds. One remarkable challenge that has captured the imagination of scientists and tech enthusiasts alike is the pursuit of enabling machines to see in the dark. Have you ever tried to take a photo of a starry sky with your smartphone? I have, and it sucked. The world where cameras can capture clear, detailed images even in extreme low-light conditions is already becoming a reality, thanks to groundbreaking research in the field of low-light image enhancement.

Are you already familiar with a revolutionary article titled “Learning to See in the Dark”? No? Well, hold onto your flashlights because we’re diving into the wild world of making cameras smarter in the dark! The study, authored by Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun, showcased an algorithm that could significantly improve the quality of images captured in low-light conditions. This development opened new frontiers in applications ranging from surveillance and photography to autonomous vehicles and beyond.

What’s the deal?

The primary challenge addressed by the researchers was the inherent noise and lack of details in images taken in low-light scenarios. Traditional cameras fumble like lost puppies when the available light is minimal, leading to grainy and indistinct photographs. The “Learning to See in the Dark” algorithm sought to overcome these limitations by harnessing the great power of deep learning.

Take a look at the comparison of low light camera outputs and the result using the algorithm from the article.

Comparison of image output of 2 cameras and the output of the convolutional neural network


The quality of the resulting image is fascinating!

How does it work?

At the heart of this groundbreaking technology is a convolutional neural network (CNN), a type of artificial neural network inspired by the human brain’s visual processing system. The CNN was trained on a massive dataset of paired short and long exposed images, learning to recognize patterns that could later be applied to enhance dark short exposed images.

One of the key features of this algorithm is its ability to balance noise reduction and detail preservation. Previous attempts at low-light image enhancement often resulted in over-smoothing, sacrificing crucial details in the process. The “Learning to See in the Dark” algorithm, however, demonstrated a remarkable capability to find a balance, producing images that were not only brighter but also retained sharpness and clarity.

Notice how grainy is the result of a BM3D scaling method used in low-light mode for modern cameras.

Comparison of BM3D denoising and the CNN technique


Why does it matter?

The applications of this technology are far-reaching. In surveillance, for instance, security cameras equipped with this algorithm could effectively operate in low-light conditions, providing law enforcement and security personnel with enhanced visibility during the night. Similarly, photographers could capture stunning images without the need for artificial lighting, preserving the ambiance and mood of a scene.

Autonomous vehicles stand to benefit significantly from this breakthrough as well. Driving at night poses challenges for self-driving cars, as their sensors often rely on visible light to navigate. With the ability to “see in the dark,” these vehicles could navigate more confidently, making nighttime driving safer and more reliable.

Is it perfect?

Of course, it’s not. Despite its remarkable achievements, the “Learning to See in the Dark” algorithm is not without its limitations. Any model’s performance is dependent on the quality and diversity of the training data, and as the dataset used for training the model does not contain humans or dynamic object it may struggle enhancing photos of those. Another opportunity for future work is runtime optimization as the current pipeline takes 0.38-0.66 seconds to process an image. Obviously it is not fast enough for real-time processing of video.

As researchers continue to work on the challenge of low-light photography, we can anticipate even more sophisticated algorithms. The fusion of AI and photography holds the promise of transforming the way we capture and perceive the world, opening new possibilities for creativity, safety, and exploration.

In conclusion, the journey to teach machines to “tame the darkness” represents a paradigm shift in artificial intelligence and computer vision. The “Learning to See in the Dark” algorithm of 2018 has paved the way for a future where low-light conditions no longer hinder our ability to capture and understand the visual world. As these advancements continue to unfold, we can look forward to a brighter – and perhaps, in this case, a clearer – future illuminated by the capabilities of artificial intelligence.

Written by Mikhail Golubkov.


Chen, C., Chen, Q., Xu, J., & Koltun, V. (2018, May 4). Learning to see in the dark. arXiv.org. https://arxiv.org/abs/1805.01934

One Reply to “Taming the darkness”

  1. Hi Mikhail!

    Just thought to let you know that the image files embedded in the text seem to be broken somehow, since the images aren’t visible in the post.

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