Intel corporation (20240296530). TEMPORAL NOISE REDUCTION ARCHITECTURE simplified abstract
Contents
TEMPORAL NOISE REDUCTION ARCHITECTURE
Organization Name
Inventor(s)
Rony Zatzarinni of Tel Aviv (IL)
Hava Matichin of Petah Tikva (IL)
TEMPORAL NOISE REDUCTION ARCHITECTURE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296530 titled 'TEMPORAL NOISE REDUCTION ARCHITECTURE
Simplified Explanation: The patent application discusses systems and methods for enhancing a temporal noise reducer (TNR) architecture to improve TNR performance and image quality in video processing pipelines.
- Motion analysis and blending are the two main steps in temporal noise reduction.
- Motion analysis involves identifying moving elements and generating a motion map.
- Blending combines the current input image frame with the previous denoised frame.
- The architecture separates motion analysis from the blending step for improved performance.
Key Features and Innovation:
- Enhancing temporal noise reduction performance in video processing pipelines.
- Separating motion analysis and blending steps for improved TNR architecture.
- Improving image quality by optimizing the processing pipeline.
Potential Applications:
- Video processing and editing software.
- Surveillance systems.
- Broadcasting and streaming services.
Problems Solved:
- Decreasing noise in video streams.
- Enhancing image quality in motion analysis.
- Improving TNR performance.
Benefits:
- Enhanced video quality.
- Reduced noise in video streams.
- Improved motion analysis accuracy.
Commercial Applications: The technology can be applied in video editing software, surveillance systems, and broadcasting services to enhance video quality and reduce noise in video streams.
Questions about Temporal Noise Reducer (TNR) Architecture: 1. What are the main steps involved in temporal noise reduction? 2. How does separating motion analysis from the blending step improve TNR performance?
Original Abstract Submitted
systems and methods for improving a temporal noise reducer (tnr) architecture that improves tnr performance and iq. temporal noise reduction is a core feature of a video processing pipeline, where tnr can be used to decrease noise in video streams. tnrs generally includes two main steps: motion analysis and blending. motion analysis includes identifying moving elements, and can include generating a motion map indicating regions of the input image that are static versus regions with movement. blending includes blending the current input image frame with the previous temporally-denoised frame. an architecture is provided that separates the motion analysis from the blending step. in particular, the architecture includes a motion analysis block that operates on the raw image at the start of the pipeline, while the blending operation is completed on the processed image at the end of the image processing pipeline.
