Low-Resolution Video Change Detection for Ghosting-Free Noise Reduction
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Solution Overview
Problem
Temporal noise reduction in video streams is challenging due to the difficulty in distinguishing between true motion and noise, leading to artifacts like ghosting, especially in low-resolution images with limited receptive fields.
Innovation Solution
A change detection system that combines a segmentation map and a neural network to generate a robust change detection map, using a pre-processing phase to optimize input and leverage a larger effective receptive field, while maintaining semantic detail, and incorporating a classifier to distinguish between static and dynamic pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temporal noise reduction averages image portions, then noise is decreased, but moving objects exhibit smearing and ghosting effects
Solution Approach 1:
The patent segments the image into multiple regions and classifies each region as static or dynamic based on change detection. Static regions are averaged over time for noise reduction, while dynamic regions are excluded from temporal averaging. This segmentation approach allows simultaneous noise reduction and motion preservation by treating different image regions differently according to their temporal characteristics.
Solution Approach 2:
The patent applies different processing quality to different regions of the image. Static regions receive temporal noise reduction processing, while dynamic regions receive different handling to preserve motion information. This local differentiation prevents the uniform processing that causes ghosting while maintaining noise reduction benefits where appropriate.
2Measurement precision
If a classifier is used to distinguish static and dynamic pixels, then change detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies change detection and classification only to regions where it is most needed, rather than uniformly across the entire image. By using preliminary processing and selective classification, the system reduces computational complexity while maintaining accuracy in critical regions. This partial application approach balances precision requirements with computational resource constraints.
3Productivity
If low-resolution video stream is processed, then computational load is reduced, but receptive field is limited and fine details are lost
Solution Approach 1:
The patent transforms the problem from spatial dimension to temporal dimension. Instead of relying solely on spatial receptive field size in low-resolution images, the system uses temporal information from multiple frames to enhance the effective receptive field. This temporal dimension compensation allows low-resolution processing to achieve detailed change detection by aggregating information across time rather than relying only on spatial resolution.
Data Source
AI summary
Systems and methods are provided for change detection in low-resolution video streams, which can be used for applications such as high resolution video restoration and processing. The techniques effectively detect changes by leveraging a large receptive field and lightweight computation, which are achieved by working with low-resolution images. In particular, the techniques include extracting features from a change detection model and a semantic segmentation model, and integrating the extracted feature outputs from the models to produce a robust change detection map. A pre-processing phase can be employed to optimize the input for each model, ensuring minimal complexity and enhanced performance. The change detection model can be implemented as a deep neural network, and methods are provided for generating ground truth (GT) data, which semantically guides the change detection neural network to perform change detection inpainting during training.