Multi-Resolution Image Sensor Moiré Pattern Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques for removing Moiré patterns from captured images require significant computational resources, often introducing latency and compromising image quality by blurring finer details.
Innovation Solution
The method involves using low-resolution images captured with ultra-wide cameras to correct high-resolution images containing Moiré patterns, aligning the images to generate a corrected image that removes the Moiré pattern while preserving image details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If digital image processing techniques are used to mitigate Moiré patterns, then Moiré pattern reduction is achieved, but computational resources are significantly demanded and latency is introduced
Solution Approach 1:
The image sensor is segmented into multiple regions with different resolutions (first resolution and second resolution). The low-resolution regions are used specifically for detecting and removing Moiré patterns, while the high-resolution regions preserve image quality. This segmentation allows the system to apply computational resources only where needed for Moiré pattern mitigation.
Solution Approach 2:
A processor acts as an intermediary between the multi-resolution image sensor and the output image. The processor receives the combined signal from both resolution levels, separates the Moiré pattern components from the high-resolution data, and reconstructs the final image with Moiré patterns removed while preserving fine details.
2Object-affected harmful factors
If digital image processing techniques are used to mitigate Moiré patterns, then Moiré pattern reduction is achieved, but image quality is compromised by blurring finer details
Solution Approach 1:
The image sensor is segmented into multiple regions with different resolutions (first resolution and second resolution). The low-resolution regions are used specifically for detecting and removing Moiré patterns, while the high-resolution regions preserve image quality. This segmentation allows the system to apply computational resources only where needed for Moiré pattern mitigation.
Solution Approach 2:
Different regions of the image sensor have different resolution characteristics optimized for different purposes. The low-resolution regions are optimized for Moiré pattern detection, while high-resolution regions are optimized for capturing fine image details. The final image combines these local qualities to achieve both Moiré pattern removal and high image quality.
3Object-affected harmful factors
If multiple image sensors with different resolutions are used, then Moiré pattern removal capability is improved, but device complexity increases
Solution Approach 1:
Multiple image sensors with different resolutions are merged into a single integrated sensor device. The sensor includes both first-resolution regions and second-resolution regions within the same physical structure, allowing the system to benefit from multi-resolution capabilities without the complexity of coordinating separate sensor systems.
Solution Approach 2:
The integrated sensor serves multiple functions simultaneously: capturing high-resolution image data, detecting Moiré patterns, and providing the computational processing needed to combine these functions. This multi-functionality reduces device complexity compared to using separate specialized sensors and processing systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies image processing, enables smaller and more efficient image processing models, and allows for real-time correction of Moiré patterns on devices with limited computing resources, maintaining high image quality.
Implementation Method 1
a first image sensor configured to obtain a first image having a first resolution; a second image sensor configured to obtain a second image having a second resolution different than the first resolution
Data Source
AI summary
Systems and methods for image processing (e.g., image correction) are described. Embodiments of the present disclosure include image processing techniques that reduce or remove Moiré patterns by leveraging low resolution images (e.g., images captured using low resolution sensors, such as an ultra-wide camera). For instance, an image including a Moiré pattern may be corrected based on a second image having a low resolution. In one example, a device may capture a high resolution image that includes a Moiré pattern. The device may also capture a low resolution image that is aligned with the high resolution image and used to correct (e.g., remove) the Moiré pattern. In some embodiments, the systems and techniques described herein may be implemented in real-time on a user device (e.g., that includes a high resolution image sensor and a low resolution image sensor) for efficient and effective correction of Moiré patterns in image/video capture applications.