Automatic Artifact Removal in Digital Images
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Solution Overview
Problem
Conventional systems for removing artifacts from digital images captured by cameras require manual user intervention and are computationally inefficient, leading to user frustration and inefficient operation.
Innovation Solution
An automatic artifact removal system that generates a segmentation map to identify and classify contours, using multidimensional cues to differentiate between artifacts and document features, and employs object and sampling masks to automatically remove or lessen the effect of artifacts in digital images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual hole filling techniques are used to remove artifacts, then artifact removal can be achieved, but user frustration and computational inefficiency increase due to repeated user interaction requirements
Solution Approach 1:
The system performs artifact removal automatically without requiring user intervention. The computing device itself identifies artifacts, generates masks, and executes removal operations autonomously, eliminating the need for repeated user interactions with manual hole filling techniques.
Solution Approach 2:
The system performs preliminary identification and classification of artifacts before removal. By using machine learning models to detect and categorize artifacts in advance, the system prepares removal strategies beforehand, enabling efficient automated execution without user input during the actual removal process.
2Reliability
If manual artifact selection and replacement is performed, then artifact removal can be achieved, but device operation efficiency decreases due to complex user interactions
Solution Approach 1:
The system replaces manual mechanical interactions (user selection, dragging, dropping) with automated computational processes. Machine learning models and algorithms automatically identify artifacts, generate replacement content, and apply corrections, substituting user-driven mechanical operations with efficient computational workflows.
Solution Approach 2:
The system introduces intermediate processing steps including artifact detection models, classification algorithms, and mask generation processes. These intermediaries bridge the gap between raw image input and final artifact removal, enabling automated high-accuracy removal without requiring direct user manipulation.
3Reliability
If conventional image editing applications are used, then artifact removal can be performed, but the system requires technical proficiency that not all users possess
Solution Approach 1:
The system automatically detects and removes artifacts without requiring users to understand or operate complex image editing tools. The computing device performs all operations autonomously, making artifact removal accessible to users regardless of their technical proficiency with image editing applications.
Solution Approach 2:
The system segments the complex artifact removal process into automated sub-tasks: artifact detection, classification, mask generation, and content synthesis. By dividing the process and automating each segment, the system eliminates the need for users to manually perform multiple complex steps in image editing applications.
4Reliability
If manual hole filling is used to replace artifacts, then artifact removal can be achieved, but computational efficiency is reduced due to repeated user interactions
Solution Approach 1:
The system performs preliminary artifact identification and classification using trained machine learning models before executing removal operations. By detecting and categorizing artifacts in advance, the system avoids repeated computational passes and user-triggered operations, reducing overall energy consumption while maintaining removal quality.
Solution Approach 2:
The system replaces energy-intensive manual interactions (repeated selection, sampling, and application operations) with optimized computational algorithms. Automated mask generation and content synthesis replace the need for multiple user-driven hole filling operations, reducing computational energy consumption while achieving reliable artifact removal.
Data Source
AI summary
Techniques and systems are described for automatic artifact removal in a digital image. A segmentation map is generated that describes a magnitude of difference among pixels in a digital image. Contours may be generated that describe boundaries of objects described in the segmentation map. The contours may be filtered according to two-dimensional and three-dimensional cues to identify contours corresponding to artifacts in the digital image. For each contour corresponding to an artifact, an object mask and a sampling mask may be generated. The object mask and the sampling mask may be utilized as part of a content filling operation upon the digital image to remove the artifact, and a corrected digital image is generated that does not include the artifact.