Automatic Lighting Effect Removal Using Segmentation Masks
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Solution Overview
Problem
Conventional lighting effect removal techniques in image editing rely on user input, making the process time-consuming and tedious, and often result in unrealistic images due to difficulties in accurately capturing the true skin tone, leading to overcompensation and loss of local facial details.
Innovation Solution
An image delighting system that uses a machine learning lighting removal network, conditioned with a separation mask, segmentation mask, and skin tone mask, to automatically remove shadows and highlights from images, allowing user input for fine-tuning the lighting representation to maintain realistic skin tones and facial details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional model-based techniques are used to remove lighting effects, then the process is automated, but the resulting images appear unrealistic or computer-generated due to overcompensation and loss of local facial details
Solution Approach 1:
The system applies different processing strategies to different regions of the image. Specifically, it identifies skin regions versus non-skin regions and applies targeted adjustments only where needed, preserving local facial details while removing lighting effects. The machine learning model learns region-specific characteristics to maintain realism in skin tones and facial features.
Solution Approach 2:
The system performs preliminary identification and segmentation of skin regions before applying lighting effect removal. By first detecting and isolating skin areas using the separation mask and skin tone mask, the system can then apply targeted adjustments that preserve local details while removing unwanted lighting effects from specific regions.
2Manufacturing precision
If manual user input techniques are used to edit lighting effects, then skin tone accuracy can be maintained, but the process becomes time-consuming and tedious
Solution Approach 1:
The system automatically performs skin tone identification, region segmentation, and lighting effect removal without requiring manual user input for each adjustment. The machine learning model self-adjusts parameters based on learned patterns from training data, maintaining skin tone accuracy while eliminating the time-consuming manual editing process.
Solution Approach 2:
The system uses feedback from the machine learning model to automatically adjust lighting effect removal parameters. The model continuously refines its output based on the identified skin regions and desired skin tone characteristics, enabling automated precise adjustments without manual intervention.
3Productivity
If automated lighting effect removal is applied uniformly across the image, then processing speed increases, but local facial details and skin tone accuracy are lost
Solution Approach 1:
The system applies different processing strategies to different regions of the image. Specifically, it identifies skin regions versus non-skin regions and applies targeted adjustments only where needed, preserving local facial details while removing lighting effects. The machine learning model learns region-specific characteristics to maintain realism in skin tones and facial features.
Solution Approach 2:
The system segments the image into distinct regions including skin regions, non-skin regions, and different facial features using separation masks and segmentation masks. This segmentation enables the system to process each region with appropriate parameters, maintaining local details while achieving overall automation and speed.
Data Source
AI summary
In accordance with the described techniques, an image delighting system receives an input image depicting a human subject that includes lighting effects. The image delighting system further generates a segmentation mask and a skin tone mask. The segmentation mask includes multiple segments each representing a different portion of the human subject, and the skin tone mask identifies one or more color values for a skin region of the human subject. Using a machine learning lighting removal network, the image delighting system generates an unlit image by removing the lighting effects from the input image based on the segmentation mask and the skin tone mask.