I Tested Night Vision Images in a Pitch Black Rainstorm

If you have ever searched for night vision images online, you have likely been lied to.

The internet is full of perfectly crisp photos that claim to show what these devices can do. But usually, those photos come from controlled studios or simulations. They do not show you the grain. They do not show you the motion blur. And they certainly do not show you what happens when the weather gets bad.

Marketing ads sell you on the fantasy of seeing in the dark. But they rarely explain how it works or the limits you will face when you step out of your front door.

This week, I decided to ignore the spec sheets. I live in a small ski resort in the French Alps. The terrain here is rough, the weather changes fast, and when the sun goes down, it gets truly dark. It is the perfect place to test the CIGMAN CNVPRO binoculars.

My goal was not just to review a product. It was to do a real-world stress test. I wanted to answer a question that many buyers have. In a world full of talk about military image intensifier night vision and high-tech thermal imaging, where does modern digital night vision fit in? And what do the images really look like when it starts to rain?

This is what most people picture when they think of night vision. It is the green, grainy video from old war news. These devices use vacuum tubes to take tiny bits of light and multiply them thousands of times.

How it works: They use a photocathode to turn light into electrons and then amplify them.

The Reality: They are great at seeing in starlight. But they are fragile. If you turn them on during the day, you burn the tube. They are also expensive and hard to record video with.

On the other side, we have thermal imaging night vision binoculars. These do not see light at all. They see heat.

How it works: A sensor called a microbolometer detects temperature differences from objects.

The Reality: Nothing can hide from thermal. A rabbit in tall grass glows bright. But thermal lacks detail. It tells you something is there.

But it often cannot tell you who it is. It struggles with faces or reading signs because those things do not emit heat. Also, contrary to movies, thermal generally cannot see through glass.

This is what I am testing today. Digital night vision works like a powerful version of your digital camera.

It uses a CMOS sensor that is made to be very sensitive to Near-Infrared light.

The Good Part: It is versatile. You can record video, zoom in, and use it in broad daylight without breaking it.

The Catch: It relies on an active Infrared (IR) Illuminator. This is basically an invisible flashlight that creates its own light when it is too dark.

With this in mind, I took the CNVPRO out to see if a digital sensor could offer good performance for the price.

Table 1: Analog tubes vs thermal imaging vs digital night vision

To test night vision binocular range properly, looking across a flat field is not enough. I need distance. I need targets at set points to judge the quality.

I chose the bottom of the closest ski slope in my village. It is currently autumn, so there is no snow. The ground is dark earth and grass. This is a low-contrast scene that is hard for sensors to read. The main ski lift goes up the mountain and gives me perfect distance markers.

The weather forecast was bad. It called for broken clouds in the morning, turning to rain and total darkness by evening. These were perfect conditions for a hard test.

The weather forecast was bad. It called for broken clouds in the morning, turning to rain and total darkness by evening. These were perfect conditions for a hard test.

Table 2: The specific conditions I was working with

One thing people often forget is that digital night vision is useful during the day. Because it uses a digital chip instead of a glass tube, it works just like a standard pair of digital binoculars.

I started looking at 10:15 AM. The lighting was tricky. The sun was hiding behind broken clouds. This flat light often makes images look muddy on cheap sensors.

Holding the binoculars up to my eyes, the screen inside turned on. Unlike old binoculars where you look through glass, here you look at a display.

I took a photo with my smartphone first. The phone made the Alps look flat and far away. The ski lift tower was tiny.

Switching to the CNVPRO, the difference was big. The zoom pulled the 150m tower right in close.

Color:

Digital sensors sometimes get the colors wrong. But the night vision camera images I took in day mode were true to life. The definition in the trees and the lift cables were distinct.

Sharpness: 

After I turned the focus wheel, the image was quite crisp. I could clearly see the parts of the tower structure.

Just to see what would happen, I turned on the IR mode while the sun was still up. The image instantly turned black and white.

Why do this? It tests contrast. In normal light, a green deer against green grass is hard to see. But in infrared, plants reflect light differently than fur or metal.

In my test, the contrast was good. I saw that it did not pick up the detail in the leaves as well as the color mode, but the hard edges of the tower stood out. This showed me the sensor was working correctly to strip away color and focus on brightness.

I went back to the same spot at about 6:00 PM. It changed completely. The mountains block the view here, so when the sun sets, it gets very dark.

There was no moon. The clouds were thick and blocked the stars. Aside from a slight glow from an apartment building nearby, it was pitch black.

This is where image intensifier night vision struggles because there is no light to amplify. This is also where digital night vision faces its biggest test, which is digital noise.

Before turning on the IR light, I tried the Low Light Color Mode.

When light is low, the camera tries to find more light by increasing its sensitivity. This creates noise or grain.

Looking through the screen, the image was indeed quite grainy. I tried to focus better, but the lack of light made it hard.

The Result:

I could see the snow cannon at 70m, but the distant towers were lost in the dark.

Is this a failure? 

No, it is physics. Without the moon or streetlights, a camera cannot make a clear color image from nothing. This grainy result is honest.

But even with the noise, I saw something important. I could still see the orange colour of the snow cannon. This color info helps you tell the difference between a brown animal and a grey rock before you switch to IR.

Then, I pressed the IR button.

The difference was huge. It was like I turned on a spotlight that only I could see. The grainy, grey screen vanished. It was replaced by a sharp, black-and-white image.

This is the main strength of digital systems. They create their own light.

I looked at the snow cannon first. In the dark, my eyes could not see it at all. But through the binoculars, I saw the object clearly.

In the grainy color mode, I could just tell it was orange. Now in black and white, the detail was clear. I could see the orange color of the magnified snow cannon. This detail is vital for knowing what you are looking at. If you use thermal imaging, a snow cannon and a bear might look like similar blobs. But with this digital IR view, you get visual proof. As I noted, if this were a deer walking in front of me, I would be happy with the image.

I looked up at the ski tower. The IR beam went through the darkness easily.

It had started to rain, but I could still clearly see the wheels on the top of the first tower. This 150-meter mark is a great distance for digital night vision. It lets you watch wildlife without scaring them, keeping you far away while still seeing clearly.

I looked further up at the mountain hut on the slopes.

I noted that you would certainly see people or animals walking around the hut. The sharpness was not as good as the close targets because of the rain, but seeing activity at 200+ meters in total darkness is very useful for security.

Table 3: what the IR mode could actually see?

We need to talk about the rain. During my test, the drizzle turned into heavier rain.

Rain creates problems. Water absorbs infrared light, which shortens your range. Raindrops also reflect light back at the lens, creating bright streaks.

You can see this in my test video. I noted that you can see the rain coming in from the right.

But the result was surprising. Even with these problems, the sensor worked.

Trust in the Gear: I was not worried about the device getting wet. It has an IP54 rating. So I stood in the rain and kept filming.

Performance: The rain made the long-range image softer, but the targets within 150 meters stayed visible. The wheels on the tower were still clear.

This proves that for real uses, like checking your land or watching for animals, bad weather does not stop you.

After looking at the footage back inside, I thought about who this device is for.

The CIGMAN CNVPRO did not perform magic. It did not turn a rainy night into a perfect sunny day. But it gave me usable images in bad conditions.

Table 4: how it performed across all the tests?

If you are choosing between Thermal and Digital, here is a simple rule from my test:

Buy Thermal if you need to find things. If I wanted to scan the whole mountain for life, thermal is faster because body heat glows.

Buy Digital if you need to identify things. Thermal will not show you the wheels on the tower or the nozzles on a snow cannon. Digital gives you the visual detail to tell a boar from a dog.

Why This Device Works for Real Use

My night in the rain proved that digital night vision is a serious tool.

It handled the change from bright daylight to pitch darkness. It worked without stars. It worked in the rain.

The best range for this tech is the close-to-medium distance of 0 to 150 meters. Here, the IR light beats the darkness. While the rain made it harder, it did not stop the sensor. And frankly, if it works this well in a pitch-black storm, I am sure that on a clear night, the view would be even better.

[1] V. Suntharalingam et al., "Back-illuminated three-dimensionally integrated CMOS image sensors for scientific applications," in Focal Plane Arrays for Space Telescopes III, vol. 6690, 2007. DOI: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6690/1/Back-illuminated-three-dimensionally-integrated-CMOS-image-sensors-for-scientific/10.1117/12.739807.short

[2] S. K. Gaurav et al., "Seeing in the dark: A different approach to night vision face detection with thermal IR images," in CEUR Workshop Proceedings, Vol-3563, 2023. Available: https://ceur-ws.org/Vol-3563/paper_1.pdf