RGB Image Processing Pipeline for Artifact Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image processing techniques fail to adequately address image distortions and errors introduced by imaging device components, such as defective pixels and lens imperfections, and are inefficient, often causing information loss and inaccuracies in color reproduction.
Innovation Solution
An RGB image processing pipeline that includes demosaicing logic, local tone mapping, color correction, and gamma transformation to process image data in raw or RGB format, addressing distortions and errors by converting raw data into RGB format, applying spatially varying tone curves, and correcting color, while using signed pixel data to preserve information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are used, then processing speed may be maintained, but image quality deteriorates due to inadequate correction of distortions and errors from imaging components
Solution Approach 1:
The patent applies preliminary correction actions by performing defective pixel correction, lens shading correction, and noise pattern correction early in the processing pipeline before demosaicing and other operations. This preliminary action prevents propagation of errors through subsequent processing stages, improving overall image quality without requiring complex retroactive corrections.
Solution Approach 2:
The patent segments the image processing into distinct correction stages: defective pixel correction, lens shading correction, noise pattern correction, demosaicing, and sharpening. Each stage addresses specific imaging artifacts independently, allowing optimized processing for each function while maintaining overall image quality.
2Loss of information
If conventional processing operations are performed, then processing efficiency may be maintained, but information loss occurs during operations such as clipping and contrast adjustment
Solution Approach 1:
The patent changes the data representation parameter by using signed pixel data instead of conventional unsigned data. This parameter change allows the preservation of negative values during processing operations, preventing information loss from clipping while maintaining processing efficiency through standardized arithmetic operations on signed integers.
3Loss of time
If lookup tables are repeatedly loaded from memory for processing different image frames, then processing flexibility is maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary collection of noise patterns and correction parameters from reference images before processing actual image frames. These pre-collected data are stored in registers or memory, eliminating the need for repeated memory loading during frame processing while maintaining adaptability through the use of device-specific correction profiles.
4Object-affected harmful factors
If conventional demosaicing techniques are used, then processing speed is maintained, but image artifacts increase due to inadequate edge detection
Solution Approach 1:
The patent replaces conventional mechanical interpolation methods with a demosaicing approach based on gradient analysis and edge detection algorithms. By substituting simple interpolation with gradient-based methods that analyze local image characteristics, the system reduces artifacts like checkerboard patterns and rainbow effects while managing complexity through localized processing.
5Object-generated harmful factors
If conventional sharpening techniques are used, then processing efficiency is maintained, but noise amplification occurs alongside edge enhancement
Solution Approach 1:
The patent applies local quality control by performing sharpening operations that adapt to local image characteristics. The sharpening strength and method are adjusted based on local variance and edge detection results, applying stronger sharpening to edge regions while using gentler processing in smooth areas to avoid noise amplification, thereby maintaining efficiency through localized rather than uniform processing.
Data Source
AI summary
Systems and methods for processing image data in RGB format are provided. In one example, an electronic device includes memory to store image data in raw or RGB format, or both, and an RGB image processing pipeline to process the image data. Specifically, the RGB image processing pipeline may process the image data regardless of whether the image data is of raw or RGB format. The RGB image processing pipeline may include receiving logic to receive the image data in raw or RGB format and demosaicing logic to, when the receiving logic receives the image data in raw format, convert the image data into RGB format. The logic may include local tone mapping logic configured to apply spatially varying tone curves to the image data, a color correction matrix configured to correct color in the image data, and gamma logic configured to transform the image data into gamma space.