ML Noise Synthesis for Realistic Camera Grain
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Solution Overview
Problem
Existing noise modeling methods in digital movie production are limited in synthesizing realistic noise patterns, lacking randomness in color channels and failing to accurately model targeted noise sources, which results in unrealistic and distracting noise in visual effects compositing and data compression, and lack artistic control over camera settings.
Innovation Solution
A deep learning method using a generative model with noise injection for digital camera noise and film grain modeling in raw-RGB and sRGB color spaces, providing artistic control and multi-camera sensor noise modeling, which includes a machine learning model that predicts noise maps and synthesizes noise with specific artistic intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing noise modeling methods are used, then noise removal is achieved for VFX compositing and data compression, but the synthesized noise lacks randomness in color channels and produces repetitive patterns that are unrealistic and distracting
Solution Approach 1:
The patent replaces traditional mechanical/mathematical noise generation methods with a machine learning-based system. The ML model learns the complex statistical properties and distributions of real camera sensor noise and film grain from training data, then generates realistic noise patterns that adapt to different image content and camera settings, eliminating repetitive patterns while maintaining randomness in color channels.
Solution Approach 2:
The system dynamically adjusts noise synthesis parameters based on the input image characteristics and desired camera settings. By changing parameters such as noise distribution, color channel variations, and spatial frequency characteristics according to the learned model, the system produces realistic noise that matches the target camera's behavior rather than using fixed repetitive patterns.
2Adaptability or versatility
If existing noise synthesis methods are used, then basic noise addition is achieved, but the methods cannot accurately model the distribution of the targeted noise source or provide artistic control for different camera settings
Solution Approach 1:
The noise synthesis system is made dynamic by conditioning the ML model on various camera parameters including ISO sensitivity, shutter speed, and color temperature. The model adapts its noise generation behavior based on these settings, allowing artists to control the appearance of noise to match specific camera configurations or creative intentions rather than using static noise models.
Solution Approach 2:
The system performs preliminary learning from training data containing pairs of clean images and corresponding noisy images captured with specific camera settings. This preliminary action enables the model to pre-learn the noise characteristics and distributions for different camera configurations, which are then applied during synthesis without requiring real-time manual adjustments.
3Ease of operation
If manual adjustments are made to achieve realistic noise, then artistic control is improved, but the process becomes time-consuming and less automated
Solution Approach 1:
The ML-based noise synthesis system performs self-service by automatically analyzing the input image and generating appropriate noise patterns without requiring manual intervention. The model internally adjusts noise characteristics based on learned distributions and conditioning parameters, providing both automation and artistic control through programmable parameters rather than manual tweaking.
Solution Approach 2:
The system incorporates feedback mechanisms during training where the generated noise is compared against ground truth noisy images, and the model parameters are adjusted to minimize the difference. This feedback loop enables the system to automatically learn and replicate realistic noise characteristics without manual guidance during operation.
Data Source
AI summary
A system includes a hardware processor, a system memory storing a software code, and a machine learning (ML) model trained using style loss to predict image noise. The hardware processor is configured to execute the software code to receive a clean image and at least one noise setting of a camera used to capture a version of the clean image that includes noise, and provide the clean image and the at least one noise setting as a noise generation input to the ML model. The hardware processor is further configured to execute the software code to generate, using the ML model and based on the noise generation input, a synthesized noise map for renoising the clean image.