Super-Resolution Text Enhancement Using Multi-Loss Optimization
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Solution Overview
Problem
Existing image enhancement methods for Scene Text Recognition (STR) introduce a significant drop in the quality of reconstructed text, particularly in low-resolution images, which is critical in healthcare and retail applications.
Innovation Solution
A system and method for enhancing text in images using super-resolution image generation, involving the computation of detection, recognition, and gradient loss functions to optimize a super-resolution model for improved text clarity and overall image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing image enhancement methods are used, then image processing speed is improved, but text quality and recognition accuracy deteriorate
Solution Approach 1:
The patent changes the parameter space by introducing multiple loss functions (detection loss, recognition loss, gradient loss) with different weights to optimize the super-resolution model. This multi-parameter optimization approach allows the model to simultaneously improve text quality while maintaining processing efficiency, resolving the contradiction between speed and precision.
Solution Approach 2:
The patent implements feedback mechanisms through the computation of detection loss, recognition loss, and gradient loss functions that continuously evaluate the quality of enhanced text. This feedback loop allows the system to adjust enhancement parameters in real-time, ensuring high text quality without sacrificing processing speed.
2Device complexity
If existing enhancement methods are used, then processing complexity is reduced, but text recognition accuracy deteriorates
Solution Approach 1:
The patent segments the enhancement process into distinct components: detection loss computation, recognition loss computation, and gradient loss computation. Each segment addresses a specific aspect of text quality, allowing the system to achieve high recognition accuracy through modular, manageable complexity rather than a single complex method.
Solution Approach 2:
The patent creates a composite enhancement approach by combining multiple loss functions (detection loss, recognition loss, gradient loss) into a unified optimization framework. This composite method leverages the strengths of each individual loss function to achieve superior text recognition accuracy while keeping the overall system complexity manageable.
3Manufacturing precision
If super-resolution is applied to low-resolution images, then text clarity is improved, but computational requirements increase
Solution Approach 1:
The patent applies partial action by focusing the super-resolution enhancement specifically on text regions rather than uniformly processing the entire image. The detection loss function identifies text locations, and the enhancement is concentrated in these regions, reducing overall computational energy while maintaining text clarity.
Solution Approach 2:
The patent implements local quality enhancement by applying different enhancement strategies to different regions of the image. Text regions receive intensive super-resolution treatment through the gradient loss function, while non-text regions receive minimal processing, optimizing the balance between text clarity and computational energy consumption.
Data Source
AI summary
Systems and methods for enhancing text in images based on super-resolution are disclosed. A low resolution image is generated based on a high resolution image. A super resolution image is generated based on the low resolution image, using a super resolution model with a set of parameters. Based on the high resolution image and the super resolution image, a total loss function is computed based on: the set of parameters, a detection loss function, a recognition loss function, and a gradient loss function of the high resolution image and the super resolution image. A trained super resolution model is generated with an optimized set of the parameters that minimizes the total loss function. Text in at least one image is enhanced using the trained super resolution model.