Patch-Based Image Hole Filling with Semantic Mark Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing techniques struggle to effectively fill holes in images by preserving semantic information and ensuring coherence and completeness, often resulting in unnatural or incomplete image modifications.
Innovation Solution
A computer-implemented method using a patch-based optimization algorithm that incorporates user-defined semantic marks to identify additional content for image regions, ensuring coherence and completeness by distinguishing between inside and outside regions and applying constraints to guide the filling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patch-based techniques are used for hole-filling, then image processing can be performed, but semantic information is not preserved and coherence is compromised
Solution Approach 1:
The image is divided into patches, and the hole-filling process operates on these segmented regions. The algorithm processes patches individually while maintaining semantic consistency across the entire image, allowing coherent reconstruction that preserves semantic information through structured processing.
Solution Approach 2:
Different regions of the image are treated with different processing qualities based on their semantic importance. The algorithm identifies semantically significant areas and applies enhanced processing to preserve coherence and semantic accuracy in these critical regions while maintaining overall image quality.
2Ease of manufacture
If existing hole-filling techniques are applied, then missing content can be filled, but the filled regions appear unnatural or incomplete
Solution Approach 1:
The algorithm incorporates feedback mechanisms that continuously evaluate the filled regions against semantic constraints and coherence criteria. This feedback loop allows the system to refine the filled content to achieve natural appearance while maintaining semantic accuracy, preventing unnatural or incomplete results.
Solution Approach 2:
The algorithm dynamically adjusts processing parameters based on the semantic context of different image regions. By changing parameters such as patch size, similarity threshold, and reconstruction strength according to local semantic properties, the system achieves natural-looking results that are consistent with the overall image semantics.
3Reliability
If semantic marks are introduced to guide the process, then coherence and completeness can be improved, but device complexity increases
Solution Approach 1:
Semantic marks are processed as preliminary constraints that guide the hole-filling process. By incorporating these semantic annotations before the main reconstruction process, the algorithm can efficiently target semantically important regions, improving coherence and completeness without requiring complex real-time semantic analysis during processing.
Data Source
AI summary
An image is displayed in a computer system. The image includes contents having a feature visible therein. The contents have a region thereof defined to be provided with additional content in generating a modified image. An input is received comprising a semantic mark to be placed on the image. The semantic mark indicates an inside-region part inside the region and an outside-region part outside the region. The additional content for the region is determined using a patch-based optimization algorithm applied to the image. The patch-based optimization algorithm (i) identifies the additional content for the inside-region part based on the outside-region part and not on an area of the image that the semantic mark does not indicate, and (ii) identifies the additional content for a remainder of the region without being restricted to the outside-region part. The modified image having the additional content in the region is stored.