Saliency-Guided Image Editing for Realistic Distraction Removal
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Solution Overview
Problem
Existing image editing techniques struggle to effectively reduce distractions in images without requiring drastic and unrealistic edits, as they often fail to account for human visual attention and require manual supervision or trial-and-error adjustments.
Innovation Solution
A machine-learned model using a differentiable image editing operator and a saliency model trained on eye-gaze data to predict human visual attention, applying operators like recoloring, warping, and GAN to seamlessly integrate or remove distractions, guided by a pretrained saliency model without additional supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image editing techniques are used to remove distractions, then the distraction can be removed, but the edits become drastic and unrealistic
Solution Approach 1:
The saliency model is pre-trained on eye-gaze data to predict human visual attention before the actual image editing process. This preliminary training enables the system to understand which regions are distracting without requiring manual annotation during the editing phase, allowing realistic edits to be achieved more easily
Solution Approach 2:
The system uses the pre-trained saliency model to automatically identify and guide the editing of distracting regions without requiring manual supervision or trial-and-error adjustments. The saliency map self-guides the editing process to focus computational resources on regions that will most effectively reduce visual distraction while maintaining realism
2Manufacturing precision
If manual supervision or trial-and-error adjustments are used to remove distractions, then the editing can be controlled, but the process requires significant time and computational resources
Solution Approach 1:
The system generates a saliency map that provides feedback on which regions of the image are most likely to capture human visual attention. This feedback loop allows the automated editing process to precisely target distracting regions based on predicted human perception, achieving high precision without manual intervention or iterative adjustments
Solution Approach 2:
The patent replaces manual mechanical editing processes with an automated system that uses a pre-trained saliency model and optimization algorithms. The saliency model substitutes for human visual assessment, and the automated optimization process replaces manual trial-and-error adjustments, significantly reducing time and computational resources while maintaining precision
Data Source
AI summary
Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.