Non-linear Mapping Circuit for Image Blur Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital cameras often produce images with blur due to variations in the point spread function (PSF) of their objective optics, which existing methods struggle to fully compensate for, leading to suboptimal image quality.
Innovation Solution
An image restoration circuit performs non-linear mapping and linear convolution filtering on pixel values to generate output pixel streams with reduced blur, using a deconvolution filter with a kernel determined by the PSF, and optionally includes a second non-linear mapping to cancel initial mapping distortions, employing piecewise linear functions or look-up tables to mitigate artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If linear convolution filtering is applied to deblur images, then blur reduction is achieved, but artifacts appear in low-illumination regions due to noise amplification
Solution Approach 1:
The patent applies non-linear mapping to pixel values before linear convolution filtering to pre-condition the data. This preliminary transformation adjusts the distribution of pixel values to reduce noise amplification effects during subsequent deconvolution, particularly in low-illumination regions where noise is most problematic.
Solution Approach 2:
The patent transforms pixel value parameters through non-linear mapping functions that change the statistical distribution of intensities. By modifying how pixel values are represented before filtering, the system alters the noise characteristics and reduces artifact generation during blur correction.
2Illumination intensity
If non-linear mapping is applied to pixel values, then dynamic range of low-illumination pixels is improved, but distortion is introduced that requires additional processing
Solution Approach 1:
The patent introduces an intermediate non-linear mapping step that acts as a mediator between the original pixel values and the final deblurred output. This intermediate transformation improves low-illumination pixel representation while the subsequent inverse mapping serves as a corrective intermediary to remove introduced distortions.
Solution Approach 2:
The patent divides the image processing into separate stages: first applying non-linear mapping to specific pixel value ranges (particularly low-illumination regions), then applying linear filtering, and finally applying inverse mapping. This segmentation allows targeted improvement of specific pixel ranges without uniformly distorting the entire image.
3Manufacturing precision
If deconvolution filtering is used to compensate for PSF variations, then image restoration is achieved, but computational complexity increases
Solution Approach 1:
The patent replaces complex adaptive deconvolution algorithms with a simpler pipeline combining non-linear mapping and fixed linear convolution filtering. By substituting the mechanical complexity of adaptive PSF estimation and variable filtering with pre-computed mapping functions, the system achieves comparable restoration quality with reduced computational burden.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
Imaging apparatus (26) is provided for use with an image sensor (24). The apparatus includes a non-linear mapping circuit (42), which is configured to receive a raw stream of input pixel values generated by the image sensor and to perform a non-linear mapping of the input pixel values. to generate a mapped stream of mapped pixel values, and a linear convolution filter (44), which is arranged to filter the mapped stream of mapped pixel values to generate a filtered stream of filtered pixel values. Other embodiments are also described.