Multiplexed Imaging Spatial Frequency Modulation Saturated Pixel Reconstruction
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Solution Overview
Problem
Current imaging technologies face challenges in acquiring high dynamic range images and efficiently handling saturated pixels, particularly in capturing details in shadow and highlight areas, due to limitations in separating and reconstructing color components and managing spatial frequencies.
Innovation Solution
The method involves spatially modulating the response of imaging arrays to optical radiation using basis functions for transformations, applying filters that vary spatially with distinct frequencies, and employing Fourier transforms to separate and reconstruct image components, including automated methods for identifying and correcting saturated pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial modulation with multiple spatial frequencies is applied to separate image components, then image component separation and dynamic range improvement are enhanced, but device complexity and processing difficulty increase
Solution Approach 1:
The imaging system segments the optical radiation spectrum into multiple spectral bands using spatially varying filters with distinct spatial frequencies. Each spectral band is modulated at a different spatial frequency, allowing simultaneous capture of multiple image components in a single exposure. This segmentation approach enables precise separation of image components while maintaining system efficiency.
Solution Approach 2:
The patent introduces spatial frequency modulation as an additional dimension for encoding spectral information. By mapping different spectral bands to different spatial frequencies in the spatial domain, the system transforms spectral separation problems into spatial frequency domain problems, which can be efficiently solved using Fourier transforms and other signal processing techniques.
2Measurement precision
If spatially varying filters with distinct spatial frequencies are used, then spectral band separation is improved, but manufacturing complexity and filter design difficulty increase
Solution Approach 1:
The spatially varying filter structure serves multiple functions simultaneously: it separates spectral bands, modulates spatial frequencies, and encodes spectral information. This multi-functionality reduces the need for separate optical components for each function, thereby simplifying the overall manufacturing process while maintaining precise spectral separation.
Solution Approach 2:
The filter design utilizes parameter changes in spatial frequency and spectral transmission characteristics to achieve band separation. By systematically varying these parameters across the filter surface, the system achieves precise spectral separation without requiring complex multi-layer structures, making the manufacturing process more feasible.
3Measurement precision
If Fourier transforms are applied to separate spatially modulated components, then image reconstruction accuracy is improved, but computational time and processing complexity increase
Solution Approach 1:
The spatial modulation of spectral bands is performed during the image capture process itself, preparing the data in a form that facilitates efficient Fourier transform processing. This preliminary encoding of spectral information in the spatial domain reduces the computational burden during reconstruction, as the transforms operate on pre-organized data rather than raw spectral measurements.
4Measurement precision
If automated optimization methods are used to correct saturated pixels, then image quality in highlight areas is improved, but computational complexity and processing time increase
Solution Approach 1:
The automated optimization method uses feedback from the Fourier transformed image copies to iteratively refine the reconstruction of saturated pixels. By comparing the reconstructed image with the original captured data and adjusting the pixel values to minimize errors, the system achieves accurate recovery of saturated regions while maintaining computational efficiency through guided optimization.
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 enables efficient separation and reconstruction of image components, improving the dynamic range and detail capture in images by spatially modulating the imaging array's response and using Fourier transforms to separate and correct saturated pixels, resulting in enhanced image quality and light efficiency.
Implementation Method 1
acquiring image data by exposing an imaging array to optical radiation
Implementation Method 2
a filter wherein the filter transmissivity for each of a plurality of spectral bands varies spatially with a distinct spatial frequency
Implementation Method 3
compute a Fourier transform of the image data
Data Source
AI summary
An imaging method comprises acquiring image data in which image components are spatially modulated at distinct spatial frequencies, transforming the image data into the Fourier domain and separating the image components in the Fourier domain. The image components may be transformed into the spatial domain. The image components may comprise different colors. In some embodiments saturated pixels are reconstructed by performing an optimization based on differences between image copies in the Fourier domain. Imaging apparatus may perform the imaging methods.