Camera Module Image Quality Compensation via Pre-computed Parameters
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Solution Overview
Problem
Existing image optimization methods fail to effectively compensate for the quality deterioration caused by camera modules during image formation, leading to suboptimal image quality.
Innovation Solution
An information processing method that utilizes image processing parameters specific to the camera module to compensate for image quality issues, including lens quality parameters and Modulation Transfer Function (MTF) parameters, to enhance the original image quality by processing the image through matrix operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image optimization is based on pixels or color features of the image, then the image processing can be performed using conventional methods, but the optimization results do not meet actual image processing requirements and fail to compensate for camera module influences
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing image processing parameters (including degradation information and compensation parameters) for different camera modules before actual image processing. When an image needs processing, the system directly retrieves the pre-computed parameters corresponding to the specific camera module, avoiding the need to perform complex calculations during real-time image processing. This resolves the contradiction by preparing compensation data in advance, achieving accurate camera module influence compensation without adding computational complexity during the actual processing stage.
Solution Approach 2:
The patent applies parameter changes by transforming the image processing approach from direct pixel manipulation to parameter-based processing. Instead of optimizing images based solely on pixel or color features, the system uses camera module-specific parameters (such as degradation models and compensation parameters) to guide the processing. By changing from feature-based parameters to module-characteristic parameters, the system achieves accurate compensation for camera module influences while maintaining processing efficiency through parameter lookup and application.
2Reliability
If conventional image optimization methods are used, then the processing is simpler, but the image quality does not improve significantly due to inability to compensate for camera module influences
Solution Approach 1:
The patent applies the intermediary principle by introducing image processing parameters as a mediator between the camera module and the image processing algorithm. These parameters (including degradation information and compensation parameters) serve as an intermediate layer that translates camera module characteristics into actionable processing instructions. The intermediary parameters enable the system to compensate for camera module influences without requiring direct complex interactions between the camera hardware and processing algorithms, thus improving image quality while keeping the processing system manageable through standardized parameter interfaces.
Data Source
AI summary
An information processing method includes obtaining an original image, obtaining an image processing parameter corresponding to the camera module, and processing the original image to obtain a target image based on the image processing parameter. The original image is a sensing-signal-array formed at a sensor array of a camera module in response to external light passing through the lens of the camera module. The image processing parameter is configured to compensate an influence on an image quality of the original image caused by the camera module. The image quality of the target image is higher than the image quality of the original image.