Camera Noise Model Pixel Blending for Image De-noising
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Solution Overview
Problem
Existing image de-noising methods fail to effectively reduce noise in images captured by cameras, particularly due to environmental factors affecting image sensors, and often mistakenly treat motion as noise.
Innovation Solution
A camera noise model is generated in real-time using a processing unit to adjust pixel values in subsequent frames, determining blending ratios and de-noising strengths based on the noise model and differences between frames to differentiate between noise and motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional de-noising methods are applied to reduce image noise, then noise reduction is achieved, but motion areas are mistakenly treated as noise and damaged
Solution Approach 1:
The patent applies preliminary action by performing temporal de-noising before spatial de-noising. The temporal de-noising module first processes the image to reduce noise while preserving motion areas, and then the spatial de-noising module processes the result. This sequence ensures that motion areas are not mistakenly damaged because the temporal processing has already preserved them based on frame-to-frame consistency analysis.
Solution Approach 2:
The patent segments the de-noising process into two distinct modules: temporal de-noising and spatial de-noising. Each module handles different aspects of noise reduction with specialized algorithms. The temporal module focuses on consistency across frames, while the spatial module focuses on local pixel relationships, allowing each to optimize for its specific function without interfering with the other's strengths.
2Object-affected harmful factors
If temporal de-noising is applied to reduce noise based on frame differences, then noise reduction is achieved, but processing complexity increases
Solution Approach 1:
The patent merges temporal de-noising and spatial de-noising into a unified dual-module system that processes images together rather than separately. The temporal module's output becomes the spatial module's input, creating an integrated pipeline that leverages both approaches synergistically. This combination achieves superior noise reduction while managing complexity through modular design.
Solution Approach 2:
The patent applies dynamics by adaptively adjusting processing parameters based on image content and noise characteristics. The system dynamically determines the degree of temporal vs. spatial de-noising application, and adjusts blending ratios and processing intensities based on local image properties, allowing optimal performance across varying conditions without fixed rigid parameters.
3Object-affected harmful factors
If spatial de-noising is applied to reduce noise in the image, then noise reduction is achieved, but edge details are lost
Solution Approach 1:
The patent applies local quality by adjusting de-noising parameters based on local image characteristics. Different regions of the image receive different processing intensities - edge areas maintain higher detail preservation while flat regions receive stronger noise reduction. This is achieved through adaptive parameter selection that considers local variance, gradient magnitude, and other spatial features to preserve important structural information.
Data Source
AI summary
A method for generating and employing a camera noise model, performed by a processing unit, is introduced to at least contain the following steps. A camera noise model is provided. A first frame is obtained by controlling a camera module via a camera module controller. A blending ratio corresponding to each pixel value of the first frame is generated according to the camera noise model, the pixel value of the first frame and a corresponding pixel value of a second frame. A third frame is generated by fusing each pixel value of the first frame with the corresponding pixel value of the second frame according to the blending ratio. A de-nosing strength for each pixel value of the third frame is adjusted according to the blending ratio. Each pixel value of the third frame is adjusted using the corresponding de-nosing strength.