Empirical Exposure Normalization for Sensor Non-Linearity
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Solution Overview
Problem
Existing image processing techniques struggle to accurately adjust exposure for digital images captured near the dynamic range limits of imaging sensors, particularly in high dynamic range (HDR) photography, due to the non-linear response of digital image sensors at low light levels.
Innovation Solution
A method for empirically normalizing exposure by producing reduced-resolution pairs of images, calculating light-tone and dark-tone empirical scale factors, and applying these factors to adjust the exposure of darker images to align with the expected linear relationship, compensating for sensor non-linearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EXIF data values are used for exposure adjustment assuming linear luminance response, then processing is simple and fast, but accuracy deteriorates at low light levels due to sensor non-linearity
Solution Approach 1:
The patent applies preliminary action by pre-calculating empirical scale factors from a reduced-resolution pair of images (one properly exposed, one underexposed) before processing the full-resolution images. This preliminary calibration captures the sensor's non-linear response characteristics at low light levels, which are then used to guide the exposure adjustment of the underexposed image, thereby improving accuracy without requiring complex real-time processing during the main image enhancement workflow.
Solution Approach 2:
The patent substitutes the mechanical/mathematical assumption of linear luminance response (used in traditional EXIF-based processing) with an empirically measured response curve derived from actual sensor data. Instead of relying on theoretical linear scaling based on exposure values, the system uses measured empirical scale factors that reflect the true non-linear behavior of the sensor, replacing the idealized mechanical model with observed physical behavior.
2Measurement precision
If the full-resolution images are used directly for calculating scale factors, then accuracy is maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into two distinct stages: first, a preliminary calibration stage using reduced-resolution images to determine empirical scale factors, and second, the main processing stage applying these pre-calculated factors to full-resolution images. This segmentation allows the computationally intensive scale factor calculation to be performed on downsampled data, while the final image enhancement uses the pre-computed factors, thereby separating the accuracy-critical measurement task from the productivity-critical processing task.
Solution Approach 2:
The patent uses copying by creating and processing a reduced-resolution copy of the original images for the purpose of calculating empirical scale factors. This lower-resolution copy serves as a surrogate for determining the exposure characteristics, allowing the system to capture the essential non-linear response behavior without the computational burden of processing full-resolution data. The empirical scale factors derived from this copy are then applied to the original full-resolution images.
Data Source
AI summary
An automated process empirically normalizes a “dark” image by adjusting the apparent exposure to compensate for nonlinearity in the luminance response of the image sensor. The process includes receiving at least two digital images, one of the digital images having an exposure value that is greater than that of another of the digital images. A reduced-resolution pair of images is produced from the at least two digital images. At least one representative scale factor is calculated from tonal values in the two images and at least one empirical scale factor is determined by selective interpolation between the representative scale factor and a comparative scale factor. The empirical scale factor is used in a function applied pixelwise to the darker of the digital images to produce an empirically normalized digital image.