Compressed Image Raindrop Restoration Using Dual-Frequency Branches
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Solution Overview
Problem
Existing methods struggle to effectively remove raindrops from compressed images, as they fail to capture both global contextual information and high-frequency details, leading to complex hybrid distortions due to raindrop interference and image compression.
Innovation Solution
A novel transformer-based model, HFGlobalFormer, is introduced with dual branches for capturing low-frequency and high-frequency features using self-attention and high-frequency depth-wise convolution, and a low-high-attention module for adaptive fusion, within a hierarchical U-shaped encoder-decoder network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional block-based image compression methods (JPEG, JPEG 2000, BPG) are applied to reduce storage overhead and improve transmission efficiency, then transmission efficiency is improved, but high-frequency information is lost resulting in texture loss and blocking artifacts
Solution Approach 1:
The patent introduces a decomposition network as an intermediary that separates the compressed image into low-frequency background component and high-frequency detail component. This intermediary structure allows independent processing of different frequency components, enabling recovery of high-frequency information lost during compression while maintaining the compressed representation.
Solution Approach 2:
The patent segments the image processing task into two distinct pathways: one for low-frequency background restoration and another for high-frequency detail recovery. The decomposition network splits the input into different frequency components that are then processed separately through dedicated restoration networks, addressing the information loss at different frequency bands independently.
2Reliability
If existing raindrop removal methods are applied to compressed images, then raindrop removal capability is improved, but performance deteriorates due to inability to capture both global contextual information and high-frequency details
Solution Approach 1:
The patent segments the restoration task into two parallel processes: global context restoration for raindrop removal and local high-frequency detail recovery. The dual-branch architecture with separate restoration networks for background and detail components allows each branch to specialize in its specific function, achieving both reliable raindrop removal and high-quality detail restoration simultaneously.
Solution Approach 2:
The patent merges the outputs of two separate restoration processes (global background restoration and local detail recovery) through a fusion mechanism. The combined approach integrates the strengths of both pathways: the global contextual understanding for accurate raindrop removal and the high-frequency detail preservation for quality restoration.
3Measurement precision
If dual branches are used for capturing low-frequency and high-frequency features, then feature extraction capability is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using depth-wise separable convolutions in the high-frequency branch, which processes only local high-frequency details with reduced computational requirements compared to full convolution. This localized processing approach maintains feature extraction capability while reducing overall computational complexity.
Solution Approach 2:
The computational task is segmented into two branches with different complexity levels: the low-frequency branch uses standard convolutions for global context, while the high-frequency branch uses efficient depth-wise separable convolutions for local details. This segmentation allows the system to allocate computational resources efficiently based on the different requirements of each frequency component.
Data Source
AI summary
A computer-implemented method for restoring a compressed image with raindrops includes applying dual branches in a complementary manner for capturing low-frequency features and high-frequency features from the compressed image, extracting the high-frequency features by a high-frequency depth-wise convolution (HFDC) with zero-mean kernels, and fusing the low-frequency features and the high-frequency features by a low-high-attention module (LHAM) by adaptively allocating the importance of the branches among channels.