Image Sharpness Measure Using Standard Deviation and Gradient Filtering
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Solution Overview
Problem
Existing sharpness measures for images are not able to provide an absolute value indicating whether a region of interest is in focus, especially in varying light conditions and noise, and they do not accurately reflect human perception, leading to delays in image acquisition and tracking in real-time applications.
Innovation Solution
A method that calculates a sharpness measure by identifying a region of interest, scaling it to 200x100 pixels, determining thresholds based on ISO, luminance, and distance, and then filtering and normalizing the gradient map to produce a robust and absolute sharpness value that reflects human perception, even in low light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sharpness measures (gradient-based, histogram-based, correlation-based) are used to assess image focus quality, then pixel-level computations can be performed quickly, but these measures cannot provide an absolute sharpness value indicating whether a region is in focus, especially in varying light conditions and noise
Solution Approach 1:
The patent transforms the sharpness measurement approach by changing the fundamental parameters used: instead of relying on gradient magnitudes, histogram distributions, or correlation coefficients, the invention uses the standard deviation of pixel intensities within a region of interest. This parameter change enables the measurement to provide absolute sharpness values that are reliable across varying light conditions and noise levels, as standard deviation directly captures the concentration of pixel values around the mean, which correlates with focus quality
Solution Approach 2:
The patent replaces complex computational mechanics (gradient calculations, histogram analysis, correlation computations) with a simpler statistical mechanics approach (standard deviation calculation). This substitution maintains computational efficiency while improving measurement reliability by using a single, robust statistical parameter that is less sensitive to noise and lighting variations
2Measurement precision
If a focus sweep is executed to determine the best focus position by assessing each image, then the optimal focus position can be identified, but this process involves significant delay which is not acceptable in real-time image acquisition
Solution Approach 1:
The patent enables preliminary assessment of focus quality by calculating the standard deviation of pixel intensities from a single captured image. This preliminary action provides an immediate sharpness evaluation without requiring multiple images or iterative focus sweeps, allowing the system to determine whether the current focus position is adequate before proceeding with image capture or further processing
Solution Approach 2:
The standard deviation-based sharpness measure is self-sufficient and can be computed independently from a single image without requiring reference images, multiple exposures, or iterative optimization. This self-service capability eliminates the need for time-consuming focus sweeps while maintaining accurate focus assessment
3Ease of operation
If conventional sharpness measures are used in low light conditions, then pixel-level computations can still be performed, but the measures are affected by noise and do not accurately reflect human perception of image quality
Solution Approach 1:
The patent changes the measurement parameter from gradient-based or histogram-based metrics to the standard deviation of pixel intensities. This parameter is inherently more robust to noise because it measures the spread of intensity values around the mean, which remains a reliable indicator of focus quality even when individual pixel values are corrupted by noise in low light conditions
Solution Approach 2:
The patent uses a computationally inexpensive and simple parameter (standard deviation) that can be rapidly calculated from a single image without requiring complex processing or multiple measurements. This simple parameter provides reliable sharpness assessment in noisy conditions where more complex measures would be both computationally expensive and less accurate
Data Source
AI summary
A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.