Denoising is integrated into the image reconstruction process, addressing both the lack of data and the noise present in measurements.

Nanite technology in Unreal Engine minimizes visible transitions in geometry, making them statistically present but not perceptible to the viewer.

While handcrafted models for image denoising are still relevant, deep neural networks are increasingly outperforming them in various scenarios.

Nanite, a system developed for Unreal Engine 5, represents a significant optimization effort that required a deep understanding of both high-level and low-level hardware capabilities.

Neural networks are a fundamental concept in deep learning, representing a simple yet powerful way to process data through multiple layers of computation. The feedforward neural network takes an input, multiplies it by a matrix, applies a nonlinear operation, and repeats this process to transform the input into an output.

The upcoming version 11 of Tesla's software will include fundamental rewrites in the neural network architecture, enhancing its capabilities significantly, as it reduces the complexity of data processing by outputting essential vectors instead of a large bag of points that need to be assembled.

In using neural networks for problems like computer tomography, we are exploring how to combine handcrafted models with neural networks, particularly in how we feed them prior information.

The integration of handcrafted models with neural networks presents exciting opportunities for mathematicians to explore and improve image denoising techniques.

Neural architecture search allows us to generate models automatically, which can outperform human-designed models in specific tasks without human intervention.