Raw-Image Neural Processing for Camera Moiré Correction
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Solution Overview
Problem
Moiré patterns in images captured by modern camera devices with large sensors and sharp lenses, caused by aliasing due to insufficient spatial sampling rates and color filter arrays, are difficult to correct using conventional filtering techniques, leading to blurred images and complex hardware designs.
Innovation Solution
Implementing an artificial neural network (ANN) trained on raw image data to correct moiré patterns directly, allowing efficient processing without hardware filters, and maintaining image sharpness where possible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If an optical low-pass filter is applied to reduce moiré patterns, then moiré correction is achieved, but image sharpness is reduced and optical system complexity increases
Solution Approach 1:
The patent replaces the mechanical/optical low-pass filter with a digital signal processing approach. Instead of using physical optics to blur the image, the invention applies digital filtering and reconstruction algorithms to the captured signal, thereby eliminating moiré patterns while preserving the original image sharpness and detail information.
Solution Approach 2:
The invention changes the processing approach from optical domain to digital domain. By capturing the full-resolution signal without optical filtering and then applying digital signal processing techniques, the system dynamically adjusts filtering parameters based on the captured content, achieving moiré reduction without the fixed blur imposed by optical filters.
2Object-affected harmful factors
If an optical low-pass filter is applied to reduce moiré patterns, then moiré correction is achieved, but hardware complexity and cost increase
Solution Approach 1:
The patent substitutes complex optical hardware with software-based signal processing. Instead of adding physical filters, lenses, or other optical components to the camera system, the invention processes the captured digital signal to eliminate moiré patterns, significantly reducing hardware complexity and cost.
Solution Approach 2:
The invention creates a digital model or representation of the optical filtering effect through software algorithms. By simulating the behavior of an optical low-pass filter in the digital domain, the system achieves the same moiré reduction effect without requiring the physical filter hardware.
3Object-affected harmful factors
If conventional filtering techniques are applied to correct moiré patterns, then some moiré reduction is achieved, but image quality deteriorates due to blurring
Solution Approach 1:
The patent applies filtering selectively and adaptively rather than uniformly across the entire image. By identifying regions containing moiré patterns and applying correction only to those specific areas, the system removes moiré artifacts while preserving sharpness and detail in the rest of the image, thereby maintaining overall image quality.
Solution Approach 2:
The invention implements spatially varying processing where different regions of the image receive different treatment. Areas with moiré patterns undergo correction while other regions maintain their original quality, ensuring that the correction is applied only where necessary and does not degrade overall image quality.
Data Source
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AI summary
The present application relates to a method for generating a moiré-corrected image file, comprising the following steps: - capturing (110) a raw image file using an image sensor; - inputting (120) the captured raw image file into a pre-trained artificial neural network, ANN, wherein the ANN is configured to output a computed raw image file representing an image with a blurred region based on the input raw image file; - inputting (130) the computed raw image file into an image processing unit, IPU; and - calculating (140), by the IPU, an RGB image file based on the computed raw image file.