Landmark-Aligned Object Image Restoration for Real-Time Super-Resolution
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Solution Overview
Problem
Existing image super-resolution techniques face challenges in achieving high-quality restoration with real-time processing, especially when enlarging images with high magnification, and existing methods struggle with edge preservation, artifacts, and computational complexity.
Innovation Solution
A method and apparatus for restoring object images by detecting bounding-boxes, aligning objects using landmarks, improving images with learning models, and performing inverse warping to insert the improved images into the input image, while also performing pose estimation and background enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If continuous image super-resolution technique is used, then image restoration quality is improved, but computation amount increases and real-time processing becomes difficult
Solution Approach 1:
The patent divides the image restoration task into two independent segments: object region restoration and background region restoration. The object region is identified through bounding box detection and landmark-based alignment, then restored using a learning model. The background region is restored separately using a different learning model. This segmentation allows each region to be processed with appropriate methods, achieving high quality while maintaining real-time performance through parallel processing.
Solution Approach 2:
The patent applies different restoration strategies to different regions of the image based on their characteristics. The object region, which contains important semantic information, receives enhanced restoration using a learning model trained on aligned objects. The background region receives restoration using a separate model optimized for background characteristics. This local quality approach ensures that computational resources are allocated efficiently to regions that need them most, improving overall quality without uniformly increasing computation across the entire image.
2Productivity
If single image super-resolution technique is used, then processing speed is improved, but image restoration quality deteriorates
Solution Approach 1:
The patent performs preliminary actions before the main restoration process: detecting bounding boxes, identifying objects, detecting landmarks, and aligning objects to reference positions. These preliminary actions prepare the image data in a structured way that enables more efficient restoration. By pre-processing and organizing the information, the subsequent restoration using the learning model can proceed faster and with higher quality, resolving the contradiction between speed and quality.
3Productivity
If interpolation method is used, then processing speed is improved, but edge sharpness deteriorates
Solution Approach 1:
The patent replaces the traditional mechanical interpolation method with a learning-based restoration system. Instead of using mathematical interpolation formulas that inherently blur edges, the system uses a learning model trained on high-quality image data to predict and restore edges sharply. This substitution of the restoration mechanism enables both high processing speed through optimized neural network inference and high edge sharpness through learned feature representations.
4Manufacturing precision
If edge information technique is used, then edge sharpness is maintained, but restoration error increases when edge direction is incorrectly estimated
Solution Approach 1:
The patent incorporates feedback mechanisms through the learning model that continuously refines edge direction estimates and restoration results. The model is trained on accurately labeled data and can detect when edge directions are incorrect, allowing it to adjust its restoration strategy accordingly. This feedback loop ensures that edge sharpness is maintained while minimizing restoration errors, as the system learns from its own predictions and corrects deviations from expected patterns.
Data Source
AI summary
Provided are a method and an apparatus for restoring an object image, capable of restoring an image naturally by detecting positions of landmarks of an object in a bounding-box detected from an input image, performing warping to align the object at a central position or a reference position on the basis of the landmarks, improving the image using a learning model learned from the aligned object image, performing inverse warping for rotating the improved object image in an original direction or at an original angle, and inserting the inversely-warped object image into the input image. In addition, provided are a method and an apparatus for restoring an object image, capable of detecting positions of landmarks of an object in a bounding-box detected from an input image, performing pose estimation for a side object on the basis of the landmarks, and improving an image using a learning model learned from a side object image corresponding to the pose estimation result.