Multi-View Image Inpainting via 3D Patch Dictionary
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Solution Overview
Problem
Existing image inpainting techniques face challenges when applied to multi-view images, as they are computationally intensive and struggle to maintain aesthetic quality due to significant differences in viewing angles, often resulting in artifacts when occluded pixels are copied across views, and may not always be feasible to obtain a dense depth map.
Innovation Solution
A processor-implemented method for image inpainting that aligns multi-view images with respect to a reference image, computes priority values for pixels based on confidence and data terms, and creates a dictionary of image-patches including 3D rotations to systematically reconstruct regions of interest, using a sparse reconstruction framework to fill holes while preserving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image inpainting technique is applied to multi-view images, then object removal capability is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the inpainting process into distinct modules: multi-view image alignment, priority computation, dictionary construction with 3D rotations, and iterative reconstruction. This segmentation allows each module to be optimized independently, reducing overall computational complexity while maintaining versatility in object removal across multi-view images
Solution Approach 2:
The patent extends traditional 2D image inpainting to 3D by incorporating 3D rotation operations in dictionary construction. This dimensional extension enables the system to handle multi-view images effectively by considering spatial relationships across different viewing angles, improving object removal capability without proportionally increasing computational burden
2Reliability
If traditional image inpainting is used on multi-view images, then privacy protection is improved, but image quality deteriorates due to artifacts
Solution Approach 1:
The patent applies local quality principles by computing priority values for different pixels based on their specific characteristics (confidence term and data term). This allows the inpainting process to focus computational resources on critical regions while maintaining high image quality in reconstructed areas, preventing artifacts while ensuring privacy protection
Solution Approach 2:
The patent changes key parameters including priority computation metrics (confidence term, data term), dictionary construction methods (incorporating 3D rotations), and reconstruction strategies. These parameter changes enable the system to maintain privacy protection while significantly improving image quality and reducing artifacts compared to traditional methods
3Measurement precision
If dense depth map is obtained for multi-view inpainting, then reconstruction accuracy is improved, but feasibility decreases
Solution Approach 1:
The patent performs preliminary actions by aligning multi-view images and constructing a priority map before the actual reconstruction process. This preliminary preparation, combined with the iterative reconstruction approach, achieves high reconstruction accuracy without requiring computationally infeasible dense depth map generation, making the system practically feasible
Data Source
AI summary
This disclosure relates generally to image processing, and more particularly to system and method for image inpainting. In one embodiment, a method for image inpainting includes aligning a plurality of multi-view images of a scene with respect to a reference image to obtain a plurality of aligned multi-view images. A region of interest (ROI) representing a region to be removed from the reference image for image inpainting is selected. A dictionary is created by selecting image-patches from the reference image and the plurality of aligned multi-view images, and 3D rotations thereof. A priority value of each of a plurality of pixels of the ROI is created. The ROI is systematically reconstructed in the reference image based at least on the priority values of the plurality of pixels and the dictionary by computing a linear combination of two or more image-patches selected from the plurality of image-patches of the dictionary.