Wavefront Coding Image Processing with Reduced Filter Taps
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Solution Overview
Problem
Existing wavefront coding imaging systems face challenges in optimizing aspheric optics and electronics, leading to image degradation due to mismatched processing capabilities, which results in complex and costly hardware implementations, especially in applications like miniature cameras for cell phones and video conferencing.
Innovation Solution
Joint optimization of aspheric optics and electronics, including the use of reduced set filter tap values and spatially varying dynamic range, to minimize hardware complexity and cost, while maintaining high image quality, by employing specialized filter kernels that are complementary to the optical image's MTF or PSF.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical element and detector are matched to available processing capability, then image quality is improved, but hardware complexity and cost increase significantly
Solution Approach 1:
The patent changes the parameter set of filter tap values from arbitrary real numbers to a reduced set of integers with limited dynamic range. This parameter transformation enables the use of simplified processing hardware (integer arithmetic units instead of floating-point units) while maintaining adequate image quality through optimized filter design that exploits the reduced parameter space.
Solution Approach 2:
The patent employs lower-precision integer arithmetic and reduced dynamic range processing that can be implemented with simpler, cheaper hardware components. By accepting limited precision in the processing domain, the system trades computational accuracy for hardware simplicity and cost reduction, achieving acceptable image quality through clever filter design rather than high-precision computation.
2Measurement precision
If full processing capability is utilized, then image processing accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent transforms the processing parameters from high-precision floating-point values to integer values with reduced dynamic range. This parameter change enables the use of faster integer arithmetic operations and simplified filtering algorithms that require fewer computational cycles, thereby reducing processing time while maintaining adequate accuracy for the application.
Solution Approach 2:
The patent applies partial processing by using a reduced set of filter tap values rather than the full range of possible values. By implementing filtering with limited precision and a subset of the full parameter space, the system achieves sufficient processing accuracy for practical applications while dramatically reducing computational complexity and processing time.
3Device complexity
If reduced set filter tap values are used, then hardware complexity is reduced, but image processing flexibility decreases
Solution Approach 1:
The patent changes the parameter representation from continuous floating-point values to discrete integer values with reduced dynamic range. This parameter quantization reduces hardware complexity by enabling the use of simpler arithmetic logic units, while the specific integer values are carefully selected to maintain adequate filtering performance for the target application domain.
Solution Approach 2:
The patent extracts and removes the unnecessary precision and dynamic range from the filter tap values, retaining only the essential information needed for adequate image processing. By taking out the excess precision requirements, the system achieves hardware simplification while preserving the core functionality needed for wavefront coding decoding.
Data Source
AI summary
An image processing method includes wavefront coding a wavefront that forms an optical image, converting the optical image to a data stream and processing the data stream with a color-specific filter kernel to reverse effects of wavefront coding and generate a final image. Another image processing method includes wavefront coding a wavefront that forms an optical image, converting the optical image to a data stream and colorspace converting the data stream. The method separates spatial information and color information of the colorspace converted data stream into one or more separate channels and deblurs one or both of the spatial information and the color information. The method recombines the channels to recombine deblurred spatial information with deblurred color information, and colorspace converts the recombined deblurred spatial and color information to generate an output image.