MSFA Image Processing with Frequency Analysis and Sinc Interpolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-spectral filter arrays (MSFA) used to obtain images in visible and near-infrared bands result in low-resolution images due to sub-sampling of color and NIR channels, leading to artifacts and loss of high-frequency information.
Innovation Solution
A method and apparatus that analyze frequency characteristics of MSFA pattern images to remove aliasing, amplify resolutions using a sinc function with adaptively set cutoff frequencies, and combine channels to generate a high-resolution base image, while offsetting artifacts by weighting with a blur image based on pixel differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-spectral filter array (MSFA) is used to simultaneously obtain visible band and NIR band images, then both channel images can be captured at the same time, but the resolution of both images is degraded due to sub-sampling
Solution Approach 1:
The patent segments the image processing into multiple stages: aliasing removal through frequency analysis, high-resolution base image generation using sinc function interpolation, and artifact removal through multi-scale processing. This segmented approach allows simultaneous acquisition to be maintained while resolution is restored through subsequent processing steps.
Solution Approach 2:
The patent introduces an intermediary processing stage that generates a high-resolution base image from the sub-sampled MSFA data. This base image serves as an intermediary that preserves the simultaneous acquisition benefit while providing the resolution needed for high-quality output through frequency domain analysis and sinc function interpolation.
2Measurement precision
If resolution amplification is applied to MSFA pattern images, then high-frequency information can be restored, but artifacts are introduced in the reconstructed image
Solution Approach 1:
The patent converts the harmful artifacts introduced by resolution amplification into a beneficial process by using them as indicators for targeted removal. The multi-scale processing and pixel difference analysis specifically identify and remove artifacts while preserving the beneficial high-frequency information, effectively converting the harm into a quality improvement opportunity.
Solution Approach 2:
The patent changes multiple parameters during processing: applying different weights to pixels based on local variance, adjusting the cutoff frequency of the sinc function adaptively, and using multi-scale processing with different blur levels. These parameter changes allow the system to enhance high-frequency information while suppressing artifacts through adaptive control of the reconstruction process.
3Manufacturing precision
If adaptive cutoff frequency is used in sinc function for resolution amplification, then local image characteristics can be preserved, but processing complexity increases
Solution Approach 1:
The patent implements dynamic adaptivity in the cutoff frequency selection, allowing the sinc function parameters to change based on local image characteristics such as edge detection and local variance. This dynamic approach preserves local image qualities like edges and textures while managing complexity through localized rather than global processing, applying computational effort only where needed.
Data Source
AI summary
A method of processing an image includes: offsetting aliasing by analyzing frequency characteristics of at least one color channel and a NIR channel, of a MSFA pattern image; generating a high resolution base image; and offsetting an artifact from the high resolution base image by weighting the high resolution base image and a blur image, based on a pixel difference value between the blur image and the high resolution base image.