I worked in print design for over a decade and the studios were often supplied very rough or sub-optimal images digitally or on hard copy.

50,000 images from the ImageNet validation set were used in training. The technique isn't just good for old photos or those taken in low light, fast renders, or MRI images, it can spruce up images corrupted with text and similar overlaid shapes/colour blocks too.įor training purposes the researchers leveraged Nvidia Tesla P100 GPUs with the cuDNN-accelerated TensorFlow deep learning framework. You will see the deep learning based de-noising method remove noise of various types, from various inputs. It is worth a watch, as the AI is put through its paces on various photo examples and use cases. In the video above you can see the de-noising AI in action. Furthermore, the researchers say that this new approach requires less training time and can be faster in execution. 'Without ever being shown what a noise-free image looks like, this AI can remove artefacts, noise, grain, and automatically enhance your photos,' the researchers claim. Interestingly, this new approach to de-noising hasn't been trained by showing example pairs of noisy and clean images.

The green team's researchers collaborated with others from Aalto University and MIT and will present this new grain removal tool at the International Conference on Machine Learning in Stockholm, Sweden later this week. Nvidia has revealed a new deep-learning based approach to removing noise, artefacts, text and similar unwanted aspects of photos.

Nvidia removal tool