Image Processing Rearranging Feature Blocks Texture Recovery
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Solution Overview
Problem
Current image processing technologies for intelligent terminals, such as smartphones, suffer from poor image quality enhancement, particularly in low-light conditions, leading to texture loss and unreasonable texture generation due to limited receptive fields and lack of explicit semantic information and quality degradation judgment.
Innovation Solution
An image processing method that detects target areas, extracts feature maps, rearranges feature blocks in a feature space, and uses semantic layout and quality degradation information to guide processing, effectively restoring details and improving image quality by weighting and combining feature blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If preset filtering operators are used to enhance image texture, then the processing speed is improved, but the recovering effect for texture details is poor
Solution Approach 1:
The patent segments the feature map into multiple feature blocks and rearranges them in a feature space, allowing different regions to be processed independently while maintaining overall coherence. This segmentation enables detailed texture recovery without requiring the entire image to be processed at high computational cost simultaneously.
Solution Approach 2:
The patent introduces a feature space dimension by rearranging feature blocks along the channel dimension, transforming the traditional spatial-only processing into a multi-dimensional approach. This dimensional transformation allows texture details to be recovered by leveraging relationships across different feature dimensions rather than just spatial neighbors.
2Manufacturing precision
If the receptive field is increased to capture more context, then the texture generation becomes more accurate, but the computational complexity increases
Solution Approach 1:
By dividing the feature map into blocks and processing them independently in the feature space, the patent reduces the effective receptive field size for each computation while still achieving global context understanding through the rearrangement operation. This segmentation strategy lowers computational complexity compared to processing the entire image with a large receptive field.
Solution Approach 2:
The patent changes the parameter arrangement by rearranging feature blocks along the channel dimension, effectively transforming the receptive field dynamics. This parameter reorganization allows the network to capture contextual information more efficiently without requiring physically larger receptive fields, thus reducing computational complexity while maintaining accuracy.
3Manufacturing precision
If feature blocks are rearranged in feature space to restore texture details, then the image quality is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and organization into blocks before the main rearrangement operation. By pre-processing the feature map structure and preparing feature blocks in advance, the system reduces the computational burden during the critical rearrangement phase, thereby limiting the increase in processing time while still achieving high image quality restoration.
Data Source
AI summary
The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, and the image processing method includes: acquiring an input image; detecting a target area in the input image; and processing the target area, wherein the processing of the target area includes: obtaining a feature map of the target area, rearranging feature blocks in the feature map in a feature space, and obtaining an output image after the target area is processed based on the rearranged feature blocks and the feature map.