Sky Replacement via Mask Generation Network and Defringing
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Solution Overview
Problem
Current image editing techniques for replacing regions or objects in images, such as sky replacement, are time-consuming and prone to creating unwanted visual artefacts due to manual segmentation challenges and variations in image appearance, especially with complex boundaries like trees.
Innovation Solution
An image editing system that automatically generates region masks using a mask generation network, combines them with grayscale versions of other images to create defringing and region-specific layers, and combines these layers with the original image to produce a composite image, ensuring natural-looking results without halo or fringing artefacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual segmentation is used to identify sky and foreground regions, then users can precisely control the replacement areas, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary automatic segmentation to generate initial masks before user refinement. The mask generation network pre-processes the image to create region masks that users can then adjust, combining automated efficiency with manual precision control.
Solution Approach 2:
The system introduces an intermediary mask generation network that bridges automatic processing and manual editing. This intermediary component generates initial segmentations that serve as a foundation for user refinement, eliminating the need for completely manual pixel-by-pixel labeling.
2Ease of manufacture
If conventional image editing applications are used for region replacement, then basic editing functionality is provided, but unwanted visual artifacts and unnatural effects are created
Solution Approach 1:
The system segments the image into multiple regions using a mask generation network, creating distinct masks for different semantic regions (sky, foreground, trees). This segmentation allows selective replacement while preserving boundaries, preventing artifacts at region edges.
Solution Approach 2:
The system applies different processing quality levels to different regions. By generating multiple masks with varying boundary sharpness and applying region-specific processing, the system maintains natural appearance in each local area while avoiding artifacts throughout the composite image.
3Productivity
If automatic mask generation is implemented, then processing speed is improved, but the complexity of the system increases
Solution Approach 1:
The mask generation network serves multiple functions: it generates region masks, creates defringing masks, and produces edge detection maps. This multi-functionality reduces the need for separate processing components, managing system complexity while maintaining high productivity.
Solution Approach 2:
The system nests multiple processing stages within a unified architecture. The mask generation network is integrated with the defringing component and region-specific layer generation, creating a nested structure where components work together hierarchically to reduce overall system complexity.
Data Source
AI summary
The present disclosure provides systems and methods for image editing. Embodiments of the present disclosure provide an image editing system for perform image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. The original image and the replacement image (e.g., the image including a desirable object or region) include layers of masks. A sky from the replacement image may replace the sky of the image to produce an aesthetically pleasing composite image.