Auto-focus Control Using Image Statistics Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital imaging devices face challenges in addressing errors and distortions such as defective pixels, non-uniform light intensity, and noise amplification during image processing, particularly due to manufacturing defects and lens imperfections, which can result in artifacts and undesirable noise in images.
Innovation Solution
Implementing a statistics collection engine in the image signal processor to collect data on auto-white balance, auto-exposure, and auto-focus, and using pixel filters to perform color space conversions and lens shading corrections, which allows for the detection and correction of defective pixels and improvement of image quality by optimizing focal length and handling chromatic aberrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If conventional sharpening techniques are applied to enhance image detail, then image sharpness is improved, but noise appearance is increased
Solution Approach 1:
The patent applies adaptive sharpening by changing the sharpening parameters based on image content analysis. The system determines local characteristics (edges, textures, noise levels) and adjusts sharpening strength and characteristics accordingly, applying stronger sharpening to high-contrast edges while reducing or skipping sharpening in noisy regions, thus improving sharpness without excessive noise amplification
Solution Approach 2:
The patent implements feedback-based sharpening control where the system continuously monitors image quality metrics during the sharpening process. By analyzing the impact of sharpening on noise and edge preservation in real-time, the system adjusts sharpening parameters dynamically to maintain optimal balance between sharpness enhancement and noise control
2Quantity of substance
If conventional demosaicing techniques are used to interpolate color data, then color image reproduction is achieved, but edge artifacts such as aliasing and checkerboard artifacts are introduced
Solution Approach 1:
The patent applies local quality demosaicing by analyzing the local characteristics of each pixel region (edges, textures, uniform areas) and applying different interpolation strategies accordingly. Near edges, the system uses edge-aware interpolation to preserve directional information and avoid checkerboard artifacts, while in uniform regions it uses standard interpolation to maximize color accuracy, thus reducing edge artifacts while maintaining color completeness
Solution Approach 2:
The patent implements dynamic demosaicing adaptation where the interpolation method changes based on detected image features. The system dynamically switches between different demosaicing algorithms (e.g., traditional bilinear interpolation, edge-aware interpolation, anisotropic diffusion) depending on the local content, enabling optimal artifact reduction across diverse image regions
3Measurement precision
If image processing operations are performed to correct defects and improve quality, then image accuracy is improved, but processing complexity is increased
Solution Approach 1:
The patent segments the image processing pipeline into distinct functional modules: defect detection module, classification module, correction module, and quality assessment module. Each module handles specific tasks independently, making the complex processing manageable and allowing selective application of corrections based on detected issues, thus reducing unnecessary processing complexity while maintaining accuracy
Data Source
AI summary
Techniques are provided for determining an optimal focal position using auto-focus statistics. In one embodiment, such techniques may include generating coarse and fine auto-focus scores for determining an optimal focal length at which to position a lens associated with the image sensor. For instance, the statistics logic may determine a coarse position that indicates an optimal focus area which, in one embodiment, may be determined by searching for the first coarse position in which a coarse auto-focus score decreases with respect to a coarse auto-focus score at a previous position. Using this position as a starting point for fine score searching, the optimal focal position may be determined by searching for a peak in fine auto-focus scores. In another embodiment, auto-focus statistics may also be determined based on each color of the Bayer RGB, such that, even in the presence of chromatic aberrations, relative auto-focus scores for each color may be used to determine the direction of focus.