Image Signal Processing for Defective Pixel Edge Interpolation
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Solution Overview
Problem
Image sensing devices suffer from defective pixels and phase difference detection pixels that cannot capture color images, leading to reduced image quality and accuracy, particularly in high-frequency regions with complex textures.
Innovation Solution
An image signal processor that includes a gain converter, directionality strength determiner, and pixel interpolator to correct defective pixels by dividing kernels into sub-kernels, calculating gradient sums, determining half-directional patterns, and interpolating pixels based on edge patterns to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If defective pixels or phase difference detection pixels are included in the pixel array to enable autofocus function, then the autofocus capability is improved, but the image quality and correction accuracy deteriorate
Solution Approach 1:
The patent divides the target kernel into multiple sub-kernels (e.g., 8 sub-kernels) to independently analyze edge patterns in different regions. This segmentation allows the system to handle defective pixels by using surrounding valid pixel information from specific sub-kernels, thereby maintaining autofocus capability while improving correction accuracy through localized analysis.
Solution Approach 2:
The patent determines edge patterns and directionality strength for each sub-kernel independently, allowing different regions to be processed according to their specific characteristics. This local quality approach enables accurate correction of defective pixels by considering the unique edge pattern context of each region, thereby improving overall image quality while preserving autofocus functionality.
2Adaptability or versatility
If the ratio of defective pixels or phase difference detection pixels is increased to improve autofocus function, then the autofocus performance is enhanced, but the correction accuracy for these pixels deteriorates
Solution Approach 1:
By segmenting the kernel into sub-kernels, the patent can selectively use information from valid regions to correct defective pixels. The directionality strength determiner calculates gradient sums for each sub-kernel, enabling accurate edge pattern determination even when defective pixels are present, thus maintaining correction accuracy as the ratio of defective pixels increases.
Solution Approach 2:
The patent introduces intermediary structures including the sub-kernel framework and directionality strength determiner that mediate between the raw pixel data containing defective pixels and the final corrected image. This intermediary processing layer filters out the impact of defective pixels while preserving the autofocus-enhancing ratio, thereby maintaining both autofocus performance and correction accuracy.
3Device complexity
If conventional pixel interpolation methods are used, then the processing complexity is low, but the correction accuracy for defective pixels in high-frequency regions deteriorates
Solution Approach 1:
The patent segments the interpolation process into multiple stages: dividing the kernel into sub-kernels, calculating directionality strength for each sub-kernel, determining edge patterns, and then performing interpolation based on the determined patterns. This segmented approach increases processing complexity but significantly improves correction accuracy for defective pixels in high-frequency regions by considering local edge information.
Solution Approach 2:
The patent performs preliminary analysis by calculating directionality strength and determining edge patterns before performing the actual pixel interpolation. This preliminary action provides the necessary guidance for accurate interpolation, especially in high-frequency regions, by pre-identifying the structural characteristics of the image data that will guide the correction process.
Data Source
AI summary
An image signal processor, and a method for processing an image signal, include a directionality strength determiner, a half-directional pattern determiner, and a pixel interpolator. The directionality strength determiner divides a target kernel including a target pixel into a plurality of sub-kernels, and generates gradient sum information by calculating directionality strength of each of the plurality of sub-kernels. The half-directional pattern determiner determines whether a half-directional edge pattern is included in each of the sub-kernels in response to the gradient sum information. The pixel interpolator interpolates the target pixel in response to the edge pattern determined by the half-directional pattern determiner.