Spatial Temporal Noise Reduction Engine for Video Ghosting
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Solution Overview
Problem
Digital image and video processing technologies face challenges in noise reduction, highlight clipping, and dynamic range limitations, leading to suboptimal image quality due to limitations in Bayer color filter arrays and image sensor capabilities.
Innovation Solution
The implementation of a Bayer scaler, spatial temporal noise reduction engine, and global tone mapper within a camera system to preprocess and enhance image data, including Bayer scaling, clipping correction, and tone mapping to improve image quality and dynamic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise reduction processing is applied to image data, then image quality is improved, but details and artifacts may be lost
Solution Approach 1:
The patent segments noise reduction into spatial and temporal components, processing different aspects of the image data separately to preserve details while reducing noise
Solution Approach 2:
The patent applies preliminary denoising to the Bayer pattern data before demosaicing, preparing the data in advance to reduce noise propagation while maintaining detail integrity
2Illumination intensity
If exposure settings are increased to capture more light, then image brightness is improved, but highlight clipping occurs
Solution Approach 1:
The patent applies preliminary clipping correction to the Bayer pattern data before demosaicing, preventing highlight clipping from propagating through subsequent processing stages
Solution Approach 2:
The patent uses feedback mechanisms to adjust processing based on detected clipping conditions, modifying the processing parameters to preserve highlight information
3Measurement precision
If image processing operations are performed on full-resolution data, then image quality is improved, but processing time and computational load increase
Solution Approach 1:
The patent segments the image processing pipeline into distinct stages (Bayer scaling, denoising, clipping correction, demosaicing), allowing efficient processing at each stage
Solution Approach 2:
The patent performs preliminary processing operations (scaling, denoising, clipping correction) on the Bayer pattern data before the computationally intensive demosaicing step, reducing the load on subsequent processing
Data Source
AI summary
A system access a reference frame and temporally adjacent frames. For each portion of the reference image frame, the system calculates a pixel distance value between the portion of the reference image frame and a corresponding portion of each temporally adjacent image frame. If the pixel distance value indicates a potential ghosting artifact, the system computes a set of spatial noise reduction values for the image portion. Otherwise, the system computes a set of temporal noise reduction values for the image portion. The system blends the sets of computed spatial noise reduction values and the sets of computed temporal noise reduction values, and generates a modified reference image frame based on the blended set of noise reduction values.