Neural Network Training With Consistency Loss for Video Denoising
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Solution Overview
Problem
Images captured in a sequence, such as videos, exhibit noise variations leading to flickering due to differences in noise between successive frames, causing instability and irregularity in displayed content.
Innovation Solution
A neural network is trained using consistency loss to minimize noise variations across consecutive images by determining network losses based on differences between output and ground truth images, as well as consistency losses between successive output images, updating the algorithm to reduce flickering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network processes images from a sequence, then the images can be denoised and enhanced, but noise variations between successive frames cause flickering and instability in the output video
Solution Approach 1:
The patent introduces consistency loss as a feedback mechanism that compares successive output images and adjusts the neural network weights to minimize temporal variations. The consistency loss function measures the difference between consecutive frames and uses this information to refine the denoising process, ensuring temporal consistency while maintaining denoising quality.
Solution Approach 2:
The patent modifies the training objective by adding consistency loss as an additional parameter to the standard denoising loss. This changes the optimization landscape by incorporating temporal consistency constraints, causing the network to learn parameters that produce both clean images and temporally stable video output.
2Manufacturing precision
If the neural network minimizes noise in each individual image, then image quality improves, but the noise reduction may vary between frames leading to flickering
Solution Approach 1:
The patent merges the denoising objective with the temporal consistency objective into a unified loss function. By combining per-image denoising loss and between-frame consistency loss, the network simultaneously optimizes for both image quality and video stability, preventing the trade-off that would otherwise cause flickering.
Solution Approach 2:
The consistency loss provides feedback about temporal variations to the training process, enabling the network to adjust its denoising behavior to produce consistent results across frames. This feedback loop ensures that noise reduction does not introduce or amplify temporal inconsistencies.
Data Source
AI summary
A system and method for denoising a sequence of images while maintaining a consistent appearance among images displayed consecutively in the sequence. A machine learning system maps a first input image in the sequence of images to a first output image based on a neural network algorithm and determines a first network loss based on differences between the first output image and a ground truth image. The system further maps a second input image in the sequence of images to a second output image based on the neural network algorithm and determines a second network loss based on differences between the second output image and the ground truth image. The system determines a consistency loss based on differences between the first output image and the second output image and updates the neural network algorithm based on the first network loss, the second network loss, and the consistency loss.