Researchers are confident that their method will help improve the quality of astronomical and MRI images
11 July 2018
Researchers from NVIDIA, MIT and Aalto University showed how to reduce the noise level in photos using AI. The team trained its Noise2Noise system for 50,000 images from the ImageNet suite, using NVIDIA Tesla P100 graphics processors and the TensorFlow framework with cuDNN acceleration.
Usually, neural networks look for the difference between two kinds of photographs: noisy and "clean". The new method does not require the preparation of such pairs, the system only provides shots with different levels of interference for training. It determines how to improve the quality of the image, while not inferior to the old methods of correction.
"Noise" is most often found in MRI images, as well as in astronomical photos. Researchers are confident that their method will help improve the quality of such visualization.
The scientists presented their work at the International Conference on Machine Learning in Stockholm (ICML).
The system developed by the MIT and Berkeley scientists is called MoSculp and is based on artificial inteligence
21 September 2018
MoSculp, the joint work of MIT scientists and the University of California at Berkeley, is built on the basis of a neural network. The development analyzes the video recording of a moving person and generates what the creators called "interactive visualization of form and time." According to the lead specialist of the project Xiuming Zhang, software will be useful for athletes for detailed analysis of movements.
At the first stage, the system scans the video frame-by-frame and determines the position of key points of the object's body, such as elbows, knees, ankles. For this, scientists decided to resort to the OpenPose library, developed by the Carnegie Mellon University. Based on the received data, the neural network compiles a 3D model of the person in each frame, and calculates the trajectory of the motion, obtaining a "motion sculpture".
At this stage, the image, according to the developers, suffers from a lack of textures and details, so the application integrates the "sculpture" in the original video. To avoid overlapping, MoSculp calculates a depth map for the original object and the 3D model.
MoSculp 3D Model
The operator can adjust the image during the processing, select the "sculpture" material, color, lighting, and also what parts of the body will be tracked. The system is able to print the result using a 3D printer.
The team of researchers announced plans to further develop the MoSculp technology. Developers want to achieve from the processing system more than one object on the video, which is currently impossible. The creators of the technology believe that the program will be used to study group dynamics, social disorders and interpersonal interactions.
The principle of creating a 3D model based on human movements has been used before. For example, in August 2018, scientists at the same University of California at Berkeley demonstrated an algorithm that transfers the movements of one person to another.