Image Processing Device PSF Estimation via Block Segmentation
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Solution Overview
Problem
Existing image restoration methods face challenges in efficiently estimating the point spread function (PSF) for images degraded by camera shake, leading to high computational requirements and errors, especially when dealing with large images or those containing noise and motion blur.
Innovation Solution
The method involves selecting image block pairs from degraded and provisional restored images to estimate and update the PSF, using a candidate selection process based on error thresholds and clustering to reduce computational load while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is used for PSF estimation, then the accuracy of restoration is improved, but the computational amount and memory space required increase significantly
Solution Approach 1:
The image is divided into multiple blocks, and PSF estimation is performed separately for each block rather than using the entire image. This segmentation approach reduces the computational burden and memory requirements while maintaining restoration accuracy, as each block can be processed independently with smaller data sets.
Solution Approach 2:
Only the necessary portions of the image (specific blocks) are extracted for PSF estimation rather than processing the complete image. This extraction of relevant data portions reduces unnecessary computational overhead while preserving the essential information needed for accurate PSF estimation.
2Productivity
If a small number of image blocks are used for PSF estimation, then the computational load is reduced, but the accuracy of PSF estimation deteriorates
Solution Approach 1:
Different blocks of the image are processed with appropriate selection criteria to identify blocks that provide the most informative data for PSF estimation. This local quality approach ensures that the selected blocks have optimal characteristics for accurate PSF estimation while keeping the number of blocks manageable for computational efficiency.
Solution Approach 2:
Instead of using all image blocks, a carefully selected subset of blocks is used for PSF estimation. This partial action approach uses only the necessary number of blocks to achieve accurate estimation without the excessive computational cost of processing all blocks, striking an optimal balance between accuracy and efficiency.
3Reliability
If traditional deconvolution methods are used on the entire image, then complete image restoration is achieved, but the processing time and computational resources increase
Solution Approach 1:
The image restoration process is segmented into block-level operations where PSF estimation and deconvolution are performed on individual blocks rather than the entire image at once. This segmentation dramatically reduces processing time and computational resource requirements while maintaining overall restoration quality through systematic processing of all blocks.
Solution Approach 2:
The deconvolution process is applied selectively to blocks that require restoration rather than uniformly processing the entire image. This partial action approach reduces unnecessary processing time for blocks that may not require intensive restoration, thereby optimizing the overall processing time while maintaining restoration quality where needed.
Data Source
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AI summary
An image processing device extracts, in an image block selection section 226, a plurality of image block pairs from a degraded image and a provisional restored image, each of the image block pairs being formed by two image blocks at identical coordinates in the degraded image and the provisional restored image, and estimates a point spread function (PSF) for each of the image block pairs. From among the estimated PSFs, a PSF which is estimated to be close to a true PSF is selected as a candidate PSF. The estimation of the PSF is carried out on a block-by-block basis, whereby the amount of computation required for the estimation of the PSF can be reduced. Also, the estimation method used is not disturbed by an image block which includes noise, and therefore, a PSF which is close to the true PSF can be estimated.