Raw Image Processing Pipeline Using Signed Data Conversion
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Solution Overview
Problem
Conventional image processing techniques fail to adequately address image distortions and errors introduced by imaging device components, such as defective pixels, lens imperfections, and sensor noise, often leading to artifacts like aliasing and loss of image information, especially in high-contrast images.
Innovation Solution
The system employs an image signal processor with data conversion logic and a raw image processing pipeline that includes black level compensation, fixed pattern noise reduction, temporal filtering, defective pixel correction, spatial noise filtering, lens shading correction, and highlight recovery, all operating on signed image data to preserve negative noise and improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing techniques are used, then processing speed is maintained, but image quality deteriorates due to artifacts and loss of image information
Solution Approach 1:
The patent applies preliminary action by performing black level compensation and defective pixel correction before other processing operations. This ensures that noise and artifacts are addressed early in the processing pipeline, preventing propagation of errors through subsequent operations and preserving image information integrity.
Solution Approach 2:
The patent introduces an intermediary signed data format that preserves negative noise information. This intermediary representation allows the system to maintain full dynamic range during processing, acting as a mediator between raw sensor data and final processed output, thereby preventing information loss.
2Reliability
If conventional image processing techniques are used, then processing simplicity is maintained, but image quality deteriorates due to inadequate noise reduction and artifact correction
Solution Approach 1:
The patent segments the image processing pipeline into distinct functional blocks: black level compensation, defective pixel correction, fixed pattern noise reduction, and other processing stages. Each segment handles a specific type of noise or artifact, allowing complex processing to be organized into manageable, targeted operations that improve image quality without overwhelming system complexity.
Solution Approach 2:
The patent applies local quality by using different processing strategies for different parts of the image data. For example, defective pixel correction uses local neighborhood information to replace bad pixels, while fixed pattern noise reduction applies spatially-varying corrections. This localized approach improves image quality by addressing specific problems where they occur.
3Reliability
If conventional image processing techniques are used, then processing efficiency is maintained, but image quality deteriorates due to spreading distortions to other areas
Solution Approach 1:
The patent converts harmful black level noise into beneficial information by preserving negative values in the signed data format. This allows the system to distinguish between actual signal and noise, transforming what would be harmful artifacts into correctable data that improves overall image quality after processing.
Solution Approach 2:
The patent applies preliminary anti-action by correcting defective pixels and compensating for black level offset before other processing operations. This preemptive correction prevents distortion propagation to other image areas, addressing the root cause of artifacts before they can spread through the processing pipeline.
4Measurement precision
If conventional image processing techniques are used, then processing speed is maintained, but measurement precision deteriorates due to inadequate noise statistics collection
Solution Approach 1:
The patent performs noise statistics collection as a preliminary action during image acquisition. By gathering noise characteristics data before main processing operations, the system establishes accurate noise models that improve subsequent noise reduction effectiveness without adding significant processing time during critical image rendering stages.
Data Source
AI summary
Systems and methods for processing raw image data are provided. One example of such a system may include memory to store image data in raw format from a digital imaging device and an image signal processor to process the image data. The image signal processor may include data conversion logic and a raw image processing pipeline. The data conversion logic may convert the image data into a signed format to preserve negative noise from the digital imaging device. The raw image processing pipeline may at least partly process the image data in the signed format. The raw image processing pipeline may also include, among other things, black level compensation logic, fixed pattern noise reduction logic, temporal filtering logic, defective pixel correction logic, spatial noise filtering logic, lens shading correction logic, and highlight recovery logic.