Image Signal Processor Corner Pattern Pixel Interpolation
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Solution Overview
Problem
Existing image signal processors face challenges in accurately interpolating pixels, particularly in corner patterns, leading to noise and image quality issues due to limitations in current algorithms.
Innovation Solution
An image signal processor is designed with determiners to identify corner patterns and a pixel interpolator that uses pixel data from specific corner patterns to accurately interpolate target pixels, thereby improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predetermined pixel interpolation algorithm is used to convert the original image into a complete image, then the image can be reconstructed, but serious noise occurs in images with specific patterns due to algorithm limitations
Solution Approach 1:
The image is divided into multiple blocks, and each block is further divided into sub-blocks for individual processing. This segmentation allows the algorithm to handle different regions with appropriate interpolation methods, reducing noise while maintaining reconstruction accuracy.
Solution Approach 2:
Different interpolation methods are applied to different regions based on their characteristics. Corner pixels, edge pixels, and interior pixels are processed differently using locally adaptive interpolation that considers the specific pattern and noise characteristics of each region.
2Loss of information
If pixel interpolation is performed using neighboring pixel information, then lost pixel information can be recovered, but serious noise occurs in images with specific patterns due to algorithm limitations
Solution Approach 1:
The algorithm performs preliminary classification of pixels into different categories (corner, edge, interior) before interpolation. This preliminary action allows the system to select the most appropriate interpolation method for each pixel type, recovering lost information while avoiding noise amplification.
Solution Approach 2:
The interpolation algorithm dynamically adjusts its parameters based on the local image characteristics and noise patterns. By changing interpolation weights and method selection according to the specific region being processed, the algorithm recovers pixel information effectively while minimizing noise introduction.
3Productivity
If a simple pixel interpolation algorithm is used, then processing speed is maintained, but image quality deteriorates with serious noise in specific patterns
Solution Approach 1:
The image processing is segmented into multiple stages: block division, sub-block processing, and pixel-type classification. This segmentation allows parallel processing of different regions, maintaining high processing speed while applying quality-optimized interpolation methods to each segment.
Solution Approach 2:
The algorithm applies interpolation only where necessary based on pixel classification, rather than uniformly processing all pixels. This partial action approach maintains processing efficiency by focusing computational resources on regions that require interpolation while preserving already-valid pixel data.
Data Source
AI summary
An image signal processor capable of processing image signals and an image signal processing method for the same are disclosed. The image signal processor includes a first determiner configured to determine whether a target kernel including a target pixel corresponds to a corner pattern, a second determiner configured to determine a corner pattern group corresponding to the target kernel when the target kernel corresponds to the corner pattern, a third determiner configured to determine a target corner pattern corresponding to the target kernel from among a plurality of corner patterns of a corner pattern group corresponding to the target kernel, and a pixel interpolator configured to interpolate the target pixel using pixel data of a pixel corresponding to the target corner pattern.