Blur Segmentation Map for Image Deblurring
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Solution Overview
Problem
Existing image processing technologies struggle to effectively deblur images, particularly in cases where the blur is caused by motion and frequency components, leading to suboptimal image enhancement results.
Innovation Solution
A method and device that utilize a neural clustering model and a neural converting model to generate a blur segmentation map and convert it into an image residual error, which is then used to deblur the image by removing the blur component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image processing technologies are used for deblurring, then the processing can be performed with simple methods, but the image enhancement results are suboptimal and fail to effectively remove motion and frequency blur components
Solution Approach 1:
The patent segments the blur characteristics into multiple predetermined categories (e.g., motion blur, defocus blur, frequency-based blur) and processes each category separately through specialized neural network pathways. This segmentation allows the system to target specific blur types with optimized processing methods, significantly improving deblurring quality while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent transforms discrete blur category labels into continuous pixel value representations through a specialized conversion layer. This parameter transformation enables the system to work with continuous image data while maintaining the structured information from categorical segmentation, thereby achieving high-quality image enhancement without the computational burden of processing high-dimensional categorical data directly.
2Measurement precision
If blur characteristics are classified into predetermined categories, then the blur segmentation can be performed effectively, but the conversion to continuous pixel values requires additional processing steps
Solution Approach 1:
The patent introduces a specialized conversion layer that acts as an intermediary between the discrete blur category segmentation and the continuous pixel value domain. This intermediary component transforms categorical blur labels into continuous representations that can be directly applied to image pixels, enabling precise blur characterization while simplifying the overall processing pipeline through a dedicated transformation step.
3Manufacturing precision
If a neural network is trained for a special purpose such as image enhancement, then it can generate accurate output for specific input patterns, but it may not generalize well to unseen blur types
Solution Approach 1:
The patent designs a universal blur processing framework that can handle multiple types of blur (motion, defocus, frequency-based) through a single integrated system. The network accepts various blur categories as input and processes them through shared computational pathways with category-specific adjustments, enabling the model to generalize effectively to unseen blur types while maintaining high accuracy for known blur categories.
Data Source
AI summary
A method and device for image enhancement based on blur segmentation are provided. The method of image enhancement includes: generating a blur segmentation map including indications of blur characteristics of respective pixels of a blur image, wherein the blur characteristics are in predetermined blur characteristic categories, and wherein the generating is performed by classifying the blur characteristic of each pixel of the blur image into one of the predetermined blur characteristic categories; converting the blur segmentation map into an image residual error corresponding to a blur component of the blur image; and generating the deblurred image based on the blur image and the image residual error.