Multi-Channel Image Restoration for Deformation Defects
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Solution Overview
Problem
Existing image processing technologies rely heavily on manual operations for restoring deformation defects in images, which are inefficient and require high skill levels.
Innovation Solution
An image processing method and apparatus that utilizes an image restoration model to repair deformation defects by fusing pixel value distribution information across multiple channels, including color, transparency, and deformation fields, enabling automatic restoration without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operations are used for restoring deformation defects in images, then restoration quality can be maintained, but processing efficiency is low and high skill levels are required
Solution Approach 1:
The image restoration model automatically performs deformation defect restoration without requiring manual operations. The system self-adjusts restoration parameters and processes images independently, eliminating the need for skilled manual intervention while maintaining high processing efficiency
Solution Approach 2:
The patent replaces manual mechanical operations with an automated image restoration model based on deep learning. The model substitutes human operators and manual processes with an intelligent system that automatically restores deformation defects, improving both efficiency and ease of operation
2Manufacturing precision
If pixel value distribution information from multiple channels is fused, then restoration quality improves, but processing complexity increases
Solution Approach 1:
The patent segments the image restoration process into multiple independent channels (color channel, transparency channel, and deformation field channel). Each channel processes specific aspects of the image separately, and their results are subsequently fused. This segmentation allows complex multi-channel processing to be broken down into manageable parts, improving restoration quality while controlling processing complexity through modular architecture
Data Source
AI summary
The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium. The method includes: inputting an original image including a deformation defect into an image restoration model to obtain first pixel value distribution information, second pixel value distribution information, and third pixel value distribution information, where a first output image includes the first pixel value distribution information used to describe pixel value distribution of the first output image in each color channel of a preset color space, the second pixel value distribution information used to describe pixel value distribution of the first output image in a transparency channel, and the third pixel value distribution information used to describe pixel value distribution of the first output image in each coordinate channel of a preset deformation field; and fusing these information to obtain a processed image.