Digital Image Hole Filling Using Guidance Image Alignment
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Solution Overview
Problem
Conventional digital image fill techniques fail to produce realistic results, especially in complex scenes, as they rely on texture and color from other parts of the image or struggle to find matching digital images from repositories, leading to unrealistic outcomes.
Innovation Solution
The use of machine learning techniques to search a digital image repository for a guidance image, align it with the hole using attention maps generated by convolutional neural networks, and blend or synthesize content to fill the hole, allowing for automatic and efficient hole filling without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fill techniques use texture and color from other portions of the digital image, then the process is simple and fast, but the result looks unrealistic for structured or complex scenes
Solution Approach 1:
A guidance digital image is introduced as an intermediary element that mediates between the source digital image and the fill result. The guidance image contains reference content from external sources (stock images, user uploads, or algorithmically generated images) that serves as a template for generating realistic fills in complex scenes, resolving the contradiction between simplicity and realism by adding this intermediate reference layer
Solution Approach 2:
The solution moves from operating solely within the 2D space of the source image to incorporating a separate guidance image dimension. By searching external repositories or generating separate guidance images, the system accesses additional dimensional space (external image repositories, parameter spaces) to find suitable reference content that cannot be obtained from the source image alone
2Reliability
If search is made through image repository to locate similar digital image, then additional functionality is supported, but it is difficult to locate sufficiently similar digital image for realistic filling
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing guidance images in repositories with metadata tags, or by pre-computing feature representations of images. This preliminary organization enables faster subsequent searches and matching, reducing the time loss while maintaining high similarity match quality through pre-established indexing and categorization systems
Solution Approach 2:
The system changes parameters by adjusting search criteria, similarity thresholds, and matching algorithms dynamically. By modifying search parameters such as relevance weights, similarity thresholds, and query formulations, the system can efficiently locate sufficiently similar guidance images without exhaustive searching, balancing match quality with time efficiency
3Reliability
If machine learning techniques are used to search and align guidance digital image, then realistic fills are generated, but computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct modular stages: guidance image search, alignment parameter generation, attention map creation, and fill synthesis. Each stage is handled by specialized machine learning components (image search model, alignment model, attention mechanism, synthesis model), which can be independently optimized and processed, reducing overall computational complexity while maintaining high accuracy in feature matching
Data Source
AI summary
Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.