Depth-Aware Photo Editing With Segmentation and Depth Maps
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Solution Overview
Problem
Existing image processing technologies lack efficient methods for depth-aware editing that utilize both depth maps and segmentation data to enhance image editing capabilities, particularly in real-time applications and post-processing.
Innovation Solution
A method and system that combines depth maps and segmentation data to perform depth-aware image processing, utilizing machine learning techniques such as convolutional neural networks (CNNs) for real-time image segmentation and depth estimation, enabling features like selective object removal, blurring, zoom, and augmented reality effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image processing methods are used, then the processing speed is fast, but the editing precision and depth awareness are insufficient
Solution Approach 1:
The patent segments the image processing task into multiple independent modules: depth map generation, segmentation data generation, and various editing operations (object removal, blurring, zoom). Each module processes specific portions of the image or specific tasks separately, allowing for precise depth-aware editing while maintaining processing efficiency through parallel execution of independent operations.
Solution Approach 2:
The patent performs preliminary actions by pre-generating depth maps and segmentation data before the actual editing operations. These pre-computed depth and segmentation information are then utilized during editing tasks, enabling fast and precise depth-aware operations without recalculating depth information in real-time during each editing operation.
2Manufacturing precision
If depth maps and segmentation data are generated for every image, then the editing accuracy is improved, but the processing time increases
Solution Approach 1:
The patent generates depth maps and segmentation data as preliminary outputs before the actual editing operations. By pre-computing these foundational elements, the system avoids redundant calculations during subsequent editing tasks, thereby reducing overall processing time while maintaining high editing accuracy through the use of pre-generated depth and segmentation information.
Solution Approach 2:
The generated depth maps and segmentation data serve multiple functions simultaneously: they enable object removal, blurring, zoom operations, and augmented reality effects. This multi-functionality eliminates the need to regenerate these data structures for each different editing operation, significantly reducing processing time while maintaining accuracy across various editing tasks.
3Manufacturing precision
If multiple editing operations are applied to enhance image quality, then the output quality is improved, but the system complexity increases
Solution Approach 1:
The patent divides the image processing system into distinct functional modules: depth map generation module, segmentation data generation module, object removal module, blurring module, zoom module, and augmented reality module. Each module operates independently on specific aspects of the image, making the complex system more manageable and easier to implement while producing high-quality multi-operation edited images.
Solution Approach 2:
The system employs a universal processing framework where depth maps and segmentation data serve as common inputs for multiple different editing operations. This multi-functional approach allows the same foundational data structures to support various editing tasks (removal, blurring, zoom, AR effects), reducing overall system complexity by avoiding duplicate data generation and processing pathways for each operation type.
Data Source
AI summary
The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.