Color Interpolation Using Gradient-Based Edge Texture Detection
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Solution Overview
Problem
Existing color interpolation methods for images captured by single image sensors require expensive hardware and complex operations, leading to increased manufacturing costs and image quality issues such as distortion, blurring, and reconstruction errors, especially in sharp edges and fine structures.
Innovation Solution
An image processing method and apparatus that includes an initial interpolation unit, color difference gradient estimating unit, edge texture characteristic determining unit, and weighted interpolation unit, which estimates and modifies color information without division operations and extra buffer space, using a look-up table for dynamic weighting factors to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color interpolation methods are used, then color information can be restored, but hardware cost increases and manufacturing complexity increases
Solution Approach 1:
The patent extracts only the necessary computational operations for color interpolation, eliminating unnecessary division operations and large buffer requirements. By using a simplified algorithm that processes color difference gradients and applies lookup tables, the system achieves accurate color restoration without requiring expensive hardware resources or complex manufacturing processes.
Solution Approach 2:
The patent changes the computational parameters by using lookup tables and simplified arithmetic operations instead of complex division-based algorithms. This parameter transformation allows the system to maintain high color restoration accuracy while reducing hardware cost and manufacturing complexity significantly.
2Measurement precision
If conventional color interpolation methods are used, then color information can be restored, but image quality deteriorates due to distortion and blurring
Solution Approach 1:
The patent applies local quality by analyzing color difference gradients in different directions (up, down, left, right) and adapting the interpolation weights locally based on edge detection. This allows the system to preserve sharp edges and fine structures while avoiding the uniform blurring that occurs in conventional methods.
Solution Approach 2:
The patent implements feedback by using edge detection results to adjust interpolation weights dynamically. The system continuously refines the color restoration process based on detected edges and texture characteristics, preventing distortion and blurring while maintaining high image quality.
3Measurement precision
If conventional color interpolation methods are used, then color information can be restored, but processing time increases due to complex operations
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing interpolation weights in lookup tables during system initialization. This allows the actual color interpolation process to use simple table lookups and basic arithmetic operations, dramatically reducing processing time while maintaining high color restoration quality.
Data Source
AI summary
An image processing method includes: receiving image data from a frame buffer, wherein each pixel of the image data has only one color information; estimating four second color information corresponding to up, down, left, and right sides of the target pixel respectively according to a first color information of the target pixel per se and color information of the neighboring pixels for a target pixel of the image data; calculating four color difference gradients corresponding to up, down, left, and right sides of the target pixel respectively according to the four second color information of the target pixel; determining an edge texture characteristic of the target pixel according to the four color difference gradients of the target pixel; and determining whether to modify the bit value of the first color information of the target pixel stored in a frame buffer according to an edge texture characteristic of the target pixel.