Nvidia’s New AI Can Automatically Remove Watermarks From Photos Like A Professional

Nvidia has released their new research paper that could possibly make protecting copyright photographs even harder. Nvidia’s new AI technology can automatically fix grainy photographs.

Nvidia explains that their newest creation can automatically remove artifacts from a photograph, including text and watermarks, no matter how obtrusive they may are.



This new AI technology built by Nvidia has been trained to remove noise, grain, and other visual artifacts by studying two different versions of a photo that both feature the visual defects.

Fifty-thousand samples later, the AI can clean up photos better than a professional photo restorer.

Nvidia’s AI system is built with deep learning neural network. The team of researcher uses Nvidia Tesla P100 GPUs with the cuDNN-accelerated TensorFlow feed with a dataset containing 50,000 images.

The most impressive thing about this AI is that it can also teach itself about how to fix corrupted photos just by looking at them instead of offering it before and after photos with both corrupted and optimal.




It only requires two corrupted images to proceed with removing noise.

The AI can learn about image restoration without clean data and without ever seeing what a noise-free image looks like, this AI can remove artifacts, noise, grain, and automatically enhance photos.

The researcher also taught the AI to predict how the missing frames were supposed to look.

“It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars,” reads the paper.

The removal of watermarks is so easy for Nvidia’s AI is mainly because of its clever algorithms which make possible for a computer to zero in on the exact watermark and remove it from a photo as if it was rubbing away a smudge.


The removals work is done by identifying repeating patterns (such as watermarks) in a large collection of photos with exact same watermark you may have the same thing on your photos if you use an action to apply your watermark.

The computer can then establish a rough estimate of the watermark and what exactly it looks like by viewing the image as noise, and the watermark as the target.

The original photo is then recovered by solving what Google calls a “multi-image optimization problem.” This involves separating the watermark (the foreground) from the photo itself (the background).

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The optimization can produce very accurate estimations of the watermark’s own components and it can also deal with most watermarks seen on all kinds of photos.

The solution is relatively simple, though. The problem lies in the fact that there is consistency in watermarks across image collections.



So, to counteract the ease of removal, photographers need to somehow introduce inconsistencies for their watermarks. Even a subtle warp of your watermark in each photo is enough.

“It is possible to learn to restore signals without ever observing clean ones, at performance sometimes exceeding training using clean exemplars,” the researchers said in their paper.

“[The neural network] is on par with state-of-the-art methods that make use of clean examples using precisely the same training methodology, and often without appreciable drawbacks in training time or performance.”

“There are several real-world situations where obtaining clean training data is difficult: low-light photography (e.g., astronomical imaging), physically-based rendering, and magnetic resonance imaging,” reads the paper’s discussion section.

“Our proof-of-concept demonstrations point the way to significant potential benefits in these applications by removing the need for potentially strenuous collection of clean data.

Perhaps, the system’s best asset is that it can perform faster, sometimes rendering frames in just 7 minutes, and as well or better than professional photo restorers.

“The system is on par with state-of-the-art methods that make use of clean examples using precisely the same training methodology, and often without appreciable drawbacks in training time or performance,” reads the paper.

Nvidia’s AI is in its early stages and the system does have limitations.

The researchers point out that it can not yet detect elements unavailable in the input photos. However, the same drawbacks appsoftwaretwares that employ clean inputs.

“Of course, there is no free lunch – we cannot learn to pick up features that are not there in the input data but this applies equally to training with clean targets,” reads the paper.

In term of applications of this AI technology include cleaning up long exposure photos of the night sky taken by telescopes, as cameras used for astrophotography often generate noise that can be mistaken for stars.

The AI can also be beneficial for medical applications like magnetic resonance imaging that requires considerable post-processing to remove noise from images that are generated so that doctors have a clear image of what’s going in someone’s body.

Nvidia’s AI can cut that processing time down drastically, which in turn reduces the time needed for a diagnosis of a serious condition.

The work was developed by researchers from NVIDIA, Aalto University, and MIT. Team of Researcher includes in teams are, Jaako Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Maittala, and Timo Aila.

 

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