Image Segmentation for Mobile Photography
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Solution Overview
Problem
Mobile computing devices struggle to produce high-quality images, especially in low-light conditions or with multiple light sources, due to limitations in camera technology.
Innovation Solution
A computing device automatically segments an image into different regions and adjusts exposure levels, noise, white balance, or other characteristics for each region using a machine-learned model and edge-aware smoothing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a camera in a mobile phone is used to capture images, then portability and convenience are improved, but image quality deteriorates in certain conditions such as low-light or multiple light sources
Solution Approach 1:
The image is divided into multiple regions with different lighting characteristics using a machine-learned segmentation model. Each region is then processed independently with region-specific adjustments, allowing the system to handle complex lighting scenarios that would be difficult for a single camera to capture uniformly.
Solution Approach 2:
Different processing parameters are applied to different regions of the image based on their specific characteristics. The system adjusts exposure, white balance, and other parameters locally for each segmented region rather than applying uniform processing to the entire image, thereby improving overall image quality in challenging lighting conditions.
2Device complexity
If a single set of adjustments is applied to the entire image, then processing simplicity is improved, but image quality deteriorates because adjustments may be inappropriate for some parts of the image
Solution Approach 1:
The image is automatically segmented into multiple regions using a machine-learned model, enabling region-specific processing. This segmentation allows the system to apply appropriate adjustments to each region while maintaining automated operation, resolving the trade-off between processing simplicity and image quality.
Solution Approach 2:
The system automatically segments the image and applies appropriate adjustments to each region without requiring manual user input. The machine-learned model autonomously identifies regions and the system self-adjusts processing parameters, maintaining simplicity while improving quality.
3Manufacturing precision
If image segmentation and region-specific adjustments are applied, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent replaces manual image processing mechanics with automated machine-learned segmentation and algorithmic region-specific adjustments. This substitution reduces the perceived complexity for the user while enabling sophisticated multi-region processing that improves image quality.
4Productivity
If the entire image is processed uniformly, then processing speed is improved, but image quality deteriorates in scenes with varying lighting conditions
Solution Approach 1:
The image is segmented into regions using a machine-learned model, enabling parallel processing of different regions. This approach maintains processing efficiency while allowing each region to receive optimized adjustments appropriate to its lighting conditions, thereby improving quality without significant speed penalty.
Data Source
AI summary
A device automatically segments an image into different regions and automatically adjusts perceived exposure-levels or other characteristics associated with each of the different regions, to produce pictures that exceed expectations for the type of optics and camera equipment being used and in some cases, the pictures even resemble other high-quality photography created using professional equipment and photo editing software. A machine-learned model is trained to automatically segment an image into distinct regions. The model outputs one or more masks that define the distinct regions. The mask(s) are refined using a guided filter or other technique to ensure that edges of the mask(s) conform to edges of objects depicted in the image. By applying the mask(s) to the image, the device can individually adjust respective characteristics of each of the different regions to produce a higher-quality picture of a scene.