How large is the image (resolution, total gigapixels)?

The image has a resolution of 2 297 216 × 891 702 pixels, which equals about 2 trillion pixels. The exact number is 2 048 432 101 632 pixels. That’s roughly 2 terapixels, or over 2 000 gigapixels – an extremely detailed image where you can zoom in very far. Compared to PC-monitor: If this image were displayed on a standard PC monitor, the screen would need to be about 650 by 250 meters (2130 × 820 feet) in size — large enough to cover 23 FIFA-sized soccer fields. Compared to world’s largest camera module: The world’s largest camera module, used in the Vera C. Rubin Observatory, is about the size of a small car, weighs 2 800 kg , and captures stunning 3 200 mega pixels per image. This panorama is 626 times larger. Compared to Full HD display (1920x1080): A typical Full HD screen has about 2 million pixels. 2 trillion / 2 million = 1 000 000 times more pixels. You would need 1 000 000 (1 million) Full HD screens to display the entire image at once. Compared to 4K display (3840×2160): A typical 4K screen has about 8 million pixels. 2 trillion / 8 million = 250 000 times more pixels. You would need 250 000 4K screens to display the entire image at once. Compared to distances: If each pixel were 1×1 cm and you placed them in a single line, the total length would be 20 484 321 kilometers. That’s enough to: Circle the Earth more than 511 times, or Reach the Moon about 53 times. Compated to time: I you watched one and every pixel for 1 second, it would take you 2 048 432 101 632 seconds ≈ 65 000 years to watch the whole panorama. That’s longer than the entire span of human civilization.

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Is it cheating to use AI-algorithms to scale up the image?

There are several methods for increasing the resolution and enhancing an image using AI. Let’s start by considering this scenario: Suppose you have a very low-resolution image of a face, such as a thumbnail, no larger than perhaps 10x10 pixels. AI can use this low-resolution thumbnail to generate a high-resolution image of a face that closely matches it. This is a fact. But the question is: have you really identified the correct person based on that 10x10 pixel thumbnail? The answer, of course, is no. It has been mathematically proven that you cannot increase the details in an image without introducing more information. For example, you can slightly improve resolution by applying a sharpening algorithm, as it corrects known errors that are predictable. However, you cannot keep sharpening an already sharpened image and expect to achieve better results. Now, let’s consider a different scenario: You can create a system that learns how objects like houses, trees, and grass look. It can learn the shape of a building's corner, or recognize repeating patterns. Even without being explicitly trained to recognize letters, the system can learn about the alphabet. By training this system on real-life images, the algorithm will perform well when it needs to predict what colors to use for a pixel. In this case, more information is being added to the process by AI algorithms trained by you, which goes beyond mere guessing. There are models designed to scale up images by 2x, 4x, and 8x. However, just like with sharpening algorithms, applying the scaling process multiple times does not lead to better results.

Is it possible to get infinite resolution in the picture by taking an infinite number of images?

No, you cannot achieve infinitely high resolution by taking an infinite number of images and analyzing how light falls between pixels. The resolution is always limited by the lens’s ability to resolve details. This limitation is fundamental to the glass and construction of the lens. For instance, if you have a 300mm lens and the image is blurry, sub-pixel aligning multiple images will not make the result any sharper. Additionally, beyond the lens's limitations, one must also consider atmospheric effects. The air is constantly in motion, refracting light in all directions. This limitation is especially noticeable when photographing toward the horizon.

How to increase resolution by just taking more pictures?

The method, known as sub-pixel alignment, has been used in astrophotography for decades. Imagine a perfect scenario where a ray of light strikes the exact center of a pixel. In this case, that pixel would receive 100% of the light's intensity, while the surrounding pixels would receive nothing. Now, consider the same ray of light hitting the border between two pixels; in this situation, each pixel would receive 50% of the light's intensity. This is kind of the reverse process of anti-aliasing. Next, imagine capturing 20 images of the same object, with slight camera movements between each shot. By combining this information, you can reconstruct a super-resolution image, recovering some of the detail that would otherwise be lost if you relied on just a single exposure. Multiple slightly shifted frames from a camera can provide 1.5–2× resolution beyond the sensor’s native resolution. The biggest advantage of sub-pixel alignment is that it effectively increases the resolution and detail of an image beyond the native pixel grid. Fine textures and edges that are smaller than one pixel in a single frame can be reconstructed from multiple slightly shifted frames. Noise reduction makes these subtle details visible because the underlying signal is no longer masked by random noise.

Why do I sometime see ghosts and why do cars looks so strange?

Each image is composed of approximately 20 nearly identical images stacked on top of each other. Capturing one sequence takes about one second. The "ghosts" you see are likely caused by people moving during this time, as is the case with moving cars. Occasionally, you might notice a person's foot without the rest of their body. This happens because the foot was the only part that remained stationary during the exposure, while the rest of the body was removed by the algorithm used for stacking the images.

How long time did it take to process the images?

Here are only the processing time estimates. About one week to develop 360.000+ images from raw to tif. About one week to merge and upscale images to about 14.000 180MP super resolution images. About one week to render the final panorama. About 3 days to convert from one huge image to tiles which could be publish to webserver. Some of this processing was done several times to get best results. Before the processing could start, the images needed to be prepared. Correcting colors, haze removal and preparing for stiching took most time (weeks/months).

What is the workflow?

Group the images into sub-panoramas based on optimal suitability. Divide the images into groups, with each group dedicated to creating a 180MP super-resolution image. Develop RAW files into 16-bit TIFF format. Correct colors and remove haze for improved clarity. Sort the images within each group by sharpness, discarding the two least sharp images. Identify features to for a rough alignment of the images. Use the Enhanced Correlation Coefficient (ECC) method to align the images with sub-pixel precision. Compute the median and enhance sharpness to create a master image for optical flow correction. Apply optical flow correction to correct atmospheric turbulence. Upscale the images by 4x using Tensorflow EDSR (Enhanced Deep Residual Network). Compute the median of the corrected images. Apply an unsharp mask to restore fine details. Merge four panoramas into a single, large 360° panorama. Merge inn the sky and render the final panorama.

What kind of unexpected problems did you have?

I can imagine that the public perception of this project focuses on the challenge of saving all the images to a hard drive and then figuring out how to stitch them together. While that is indeed part of the project, it is actually one of the easier tasks. The true essence of this project lies in solving the unexpected problems that arise, and they appear at every turn. Some of the challenges include: Designing a rigid rig capable of holding the heavy lens and camera without excessive wobbling. Capturing 20 RAW images per second, hour after hour, couping with moving parts, cables, betteries, memory cards etc. Developing a storage system capable of continuously storing and moving hundreds of thousands of small files non-stop for several days. Keeping a computer running at full capacity, utilizing two RTX 4090s at it's full speed. Developing software which is capable of processing more than 380.000 images of all kind. Some algorithms struggle when there are no details in the images that can be processed. Correcting colors and removing haze from images captured on several days with warying light condition is a challenge. This panorama is made of 4 smaller panoramas. Most work in post was done on each of the 4 panoramas. But even editing 1/4th of the panorama of this size is quite a challenge. Almost every picture had to be corrected. To fit images side by side from 1/4th of the panorama you need 8K display to fit most of the images and you even need to run virtual desktop which makes your workspace even bigger. Working with images of this size requiers a vast amount of local fast storage. There actually is practial limit how much fast storage you can fit in a standard PC and this is a challenge. The cost for stepping up to pro grade servers utilizing huge amount of fast local storage is astronomical. This solution is not for our wallets. These are just few of the problems but there are many more. As we see in the final result, all of the problems can be solved.

FAQs Archive

Categories: Access, Processing
How large is the image (resolution, total gigapixels)?
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Categories: General
Where do you ship merchandise?
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Categories: Processing
Is it cheating to use AI-algorithms to scale up the image?
read more
Categories: Processing
Is it possible to get infinite resolution in the picture by taking an infinite number of images?
read more
Categories: Processing
How to increase resolution by just taking more pictures?
read more
Categories: General
How many people has been working on the project?
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Categories: Processing
Why do I sometime see ghosts and why do cars looks so strange?
read more
Categories: Processing
How long time did it take to process the images?
read more
Categories: Processing
What is the workflow?
read more
Categories: General
What kind of unexpected problems did you have?
read more

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