Content-Aware Image Enhancement Using Object Masks
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Solution Overview
Problem
Traditional image editing methods often increase detail and clarity across an entire image, leading to unwanted artifacts and uneven enhancements, particularly on mobile devices with lower quality optics, as they apply adjustments uniformly without object-specific consideration.
Innovation Solution
A content-aware enhancement system using a neural network to detect objects in images, generate masks for in-focus and out-of-focus areas, and apply localized enhancements, such as modified Laplacian effects and guided filters, to enhance details specifically in objects like people and exclude unwanted areas like sky and water.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If local contrast adjustment is applied to the entire image to improve detail and clarity, then the overall image quality is improved, but unwanted artifacts such as noise, hue shifts, and halos are introduced
Solution Approach 1:
The patent applies local contrast adjustment selectively to specific regions of the image rather than uniformly across the entire image. By identifying target regions and applying enhancement only where needed, the system improves detail and clarity in those areas while avoiding the introduction of artifacts in other regions. This regional approach allows different parts of the image to have different enhancement characteristics.
Solution Approach 2:
The patent segments the image into multiple regions based on local contrast characteristics, identifying areas that require enhancement versus areas that should remain unchanged. This segmentation allows the system to apply contrast adjustment only to specific segments (regions with insufficient local contrast) while leaving other segments untouched, thereby avoiding artifact generation in regions where enhancement is not needed.
2Manufacturing precision
If uniform enhancement settings are applied across the entire image to improve detail, then overall clarity is enhanced, but the appearance of different objects becomes inconsistent and some areas are over-enhanced
Solution Approach 1:
The patent implements local quality by determining enhancement parameters based on local image characteristics rather than applying uniform settings. The system calculates local contrast metrics for different regions and adjusts enhancement strength accordingly, ensuring that each object or region receives appropriate enhancement levels that maintain其自然 appearance and consistency.
Solution Approach 2:
The patent employs dynamic enhancement parameters that adapt to local image conditions. Rather than using fixed uniform settings, the system dynamically adjusts enhancement strength based on locally measured contrast values, allowing the enhancement process to respond to the specific characteristics of each region and maintain object appearance consistency.
3Manufacturing precision
If traditional image enhancement methods are used on mobile devices to improve image quality, then detail can be enhanced, but the process is computationally intensive and time-consuming
Solution Approach 1:
The patent applies partial action by selectively enhancing only the regions of the image that require improvement rather than processing the entire image uniformly. By identifying and targeting only areas with insufficient local contrast, the system reduces the computational workload and processing time while still achieving the desired image quality enhancement in the critical regions.
Data Source
AI summary
Exemplary embodiments are directed to a system for content-aware enhancement of an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image to detect one or more objects in the original image, generate an in-focus person mask of the original image for one or more in-focus people in the original image, and apply one or more enhancements to areas of the original image excluding the in-focus person mask.