18661987. TEMPORAL NOISE REDUCTION ARCHITECTURE simplified abstract (Intel Corporation)
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 18661987 titled 'TEMPORAL NOISE REDUCTION ARCHITECTURE
Simplified Explanation: The patent application describes a system and method for enhancing the performance and image quality of a temporal noise reducer (TNR) used in video processing pipelines.
- The architecture separates the motion analysis and blending steps of the TNR process.
- Motion analysis identifies moving elements in the input image, while blending combines the current frame with the previous denoised frame.
- The motion analysis block operates on the raw image at the beginning of the pipeline, while blending occurs at the end of the image processing pipeline.
Key Features and Innovation:
- Improved TNR performance and image quality.
- Separation of motion analysis and blending steps.
- Motion analysis block at the start of the pipeline.
- Blending operation at the end of the image processing pipeline.
Potential Applications:
- Video processing pipelines.
- Image and video noise reduction.
- Enhancing image quality in video streams.
Problems Solved:
- Decreasing noise in video streams.
- Enhancing TNR performance.
- Improving image quality in video processing.
Benefits:
- Enhanced TNR performance.
- Improved image quality.
- Better noise reduction in video streams.
Commercial Applications: The technology can be used in various commercial applications such as:
- Video production and editing.
- Surveillance systems.
- Broadcasting and streaming services.
Prior Art: Prior art related to this technology may include research papers, patents, or publications on temporal noise reduction in video processing pipelines.
Frequently Updated Research: Stay updated on research related to temporal noise reduction, video processing, and image quality enhancement for the latest advancements in the field.
Questions about Temporal Noise Reducer (TNR): 1. How does the separation of motion analysis and blending steps improve TNR performance? 2. What are the potential applications of this enhanced TNR architecture in different industries?
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.
