Multispectral Image Denoising with Sub-Band PCA and Spatial Preservation
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Solution Overview
Problem
Multispectral imaging sensors face challenges in maintaining high spectral resolution while minimizing noise, especially in low-illuminance conditions, as existing denoising methods designed for high spatial resolution images do not effectively address the unique noise characteristics of multispectral imaging.
Innovation Solution
A denoising apparatus and method that considers both spectral and spatial information by dividing the wavelength band into sub-bands, applying non-local means and bilateral filters, performing principal component analysis, and projecting eigen vectors to generate a denoised image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multispectral imaging sensor uses a filter array to simultaneously obtain light of different wavelength bands, then spectral resolution is improved, but spatial resolution is lost and noise increases
Solution Approach 1:
The patent divides the spectral information into multiple channels (first spectral information and second spectral information) and processes them separately through different denoising operations. The first denoising operation is applied to one spectral channel while the second denoising operation is applied to another spectral channel, allowing each channel to be optimized independently while maintaining overall spatial resolution
Solution Approach 2:
The patent introduces a temporal dimension by capturing images at multiple time points (first image at first time, second image at second time) and using this temporal information to enhance spatial resolution. The denoising operations utilize both spatial and temporal dimensions to recover high-resolution images that would otherwise be lost in the multispectral filtering process
2Ease of manufacture
If related art denoising methods designed for high spatial resolution images are applied to multispectral images, then the methods are simple to implement, but denoising efficiency deteriorates
Solution Approach 1:
The patent applies different denoising operations to different spectral channels based on their specific characteristics. The first denoising operation is tailored for the first spectral information while the second denoising operation is optimized for the second spectral information, allowing each operation to be optimized for its specific input characteristics rather than using a generic approach
Solution Approach 2:
The patent dynamically adjusts the denoising process by applying operations at different stages and using multiple time points. The first and second denoising operations are applied at different times based on the temporal structure of the input images, allowing the system to adapt to the specific timing and spectral characteristics of the multispectral data
3Measurement precision
If multispectral imaging sensor operates in low-illuminance conditions, then spectral information can be captured, but noise increases significantly
Solution Approach 1:
The patent merges multiple spectral channels and temporal images through the denoising operations to create a comprehensive denoised output. By combining information from multiple sources (first image, second image, first spectral information, second spectral information) through coordinated denoising operations, the system achieves effective noise reduction while preserving spectral information
Solution Approach 2:
The patent employs feedback mechanisms where the denoising operations continuously refine the spectral information. The first and second denoising operations process the spectral channels with reference to each other and to the temporal images, allowing the system to iteratively improve the quality of the spectral information while suppressing noise through cross-validation
Data Source
AI summary
There is provided an apparatus for denoising an image obtained through a multispectral imaging sensor. The apparatus includes a processor dividing a wavelength band of an input image into a plurality of sub-wavelength bands, each of the plurality of sub-wavelength bands corresponding to one of a plurality of channels, obtaining, for each of the plurality of channels, a first denoising image and a differential image by sub-sampling the input image into a plurality of sub-sampled images, obtaining, for each of the plurality of channels, a second denoising image by performing preprocessing on the differential image of the respective channel and performing principal component analysis and projection on the preprocessed a differential image of the respective channel, and generating an output image by summing the first denoising image and the second denoising image obtained for each of the plurality of channels.