Partial Convolution Image In-Painting for Irregular Holes
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Solution Overview
Problem
Existing image in-painting techniques struggle to effectively fill irregular holes in images using deep learning approaches, often resulting in artifacts like lack of texture and artificial edges, and are limited by image statistics and fixed initial pixel values.
Innovation Solution
A deep learning neural network performs partial convolution operations using a mask to identify valid and invalid pixels, updating the mask after each operation to fill irregular holes with synthesized pixel data, incorporating visual semantics and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning approaches are used for image in-painting, then visual semantics are incorporated, but artifacts such as lack of texture and artificial edges are produced
Solution Approach 1:
The patent applies local quality by using a mask to differentiate between valid and invalid pixels within the convolution kernel. Instead of treating all pixels uniformly, the mask enables selective application of convolution weights to only valid pixels, allowing the network to learn and preserve local texture characteristics while avoiding propagation of invalid data that causes artifacts.
Solution Approach 2:
The patent segments the convolution operation into partial convolution by dividing the convolution kernel into valid and invalid regions using a mask. This segmentation allows the network to process only the valid portions of the image data, preventing the propagation of invalid pixel values that would otherwise create artificial edges and texture loss in the in-painted regions.
2Ease of manufacture
If fixed initial values are used for hole pixels, then the in-painting process can proceed, but the results are skewed and produce artificial responses
Solution Approach 1:
The patent extracts the problematic fixed initial values from the in-painting process by introducing a mask that identifies and excludes invalid pixels from the convolution operation. This allows the network to perform in-painting without relying on arbitrary initial values, thereby eliminating the skewing effect and artificial responses while maintaining process feasibility.
3Ease of manufacture
If regular rectangular holes are filled, then the in-painting process is simplified, but irregular holes cannot be effectively addressed
Solution Approach 1:
The patent introduces dynamic adaptability through the mask, which can be generated to match any hole shape or configuration. The mask dynamically adapts to the specific geometry of the hole being in-painted, whether regular or irregular, allowing the same convolution-based approach to handle diverse hole shapes effectively without requiring shape-specific processing.
Data Source
AI summary
A neural network architecture is disclosed for performing image in-painting using partial convolution operations. The neural network processes an image and a corresponding mask that identifies holes in the image utilizing partial convolution operations, where the mask is used by the partial convolution operation to zero out coefficients of the convolution kernel corresponding to invalid pixel data for the holes. The mask is updated after each partial convolution operation is performed in an encoder section of the neural network. In one embodiment, the neural network is implemented using an encoder-decoder framework with skip links to forward representations of the features at different sections of the encoder to corresponding sections of the decoder.