Adaptive Wavelet Decomposition for Low-Light Detail and Noise Balance
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Solution Overview
Problem
Existing wavelet-based low-light image enhancement methods suffer from fixed decomposition schemes that fail to adapt to local image complexity, leading to inefficient processing and loss of detail, and lack cross-scale communication, resulting in suboptimal noise reduction and computational inefficiency.
Innovation Solution
A hierarchical adaptive wavelet decomposition system with cross-scale feature fusion that dynamically adjusts processing depth based on local image complexity, implements cross-scale feature sharing, and allocates computational resources efficiently, using a recursive adaptive wavelet network architecture and specialized neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If fixed wavelet decomposition schemes are used to process low-light images, then noise reduction is achieved, but detail loss occurs and processing efficiency decreases
Solution Approach 1:
The patent implements dynamic decomposition depth adjustment where the wavelet decomposition level is adaptively determined based on local image characteristics. Complex regions with high-frequency content undergo deeper decomposition for effective noise removal, while smooth regions use shallower decomposition to preserve details. This dynamic approach replaces fixed decomposition schemes, allowing the system to optimize the balance between noise reduction and detail preservation for each local region.
Solution Approach 2:
The system applies different processing strategies to different regions of the image based on local complexity assessment. High-frequency regions with fine details receive customized decomposition depths and processing parameters, while low-frequency smooth regions receive different treatment. This local quality principle ensures that each region is processed with appropriate complexity, preventing detail loss in important areas while effectively reducing noise in suitable regions.
2Device complexity
If fixed decomposition levels are applied uniformly across the entire image, then processing simplicity is maintained, but processing efficiency decreases and detail loss occurs in complex regions
Solution Approach 1:
The patent introduces dynamic decomposition depth control that adapts to local image complexity. The system automatically determines the appropriate decomposition level for each region based on frequency content analysis, rather than applying a fixed uniform level. This dynamic adjustment optimizes processing efficiency by avoiding unnecessary deep decomposition in simple regions while providing sufficient processing depth in complex regions, thereby improving overall productivity without excessive complexity increase.
Solution Approach 2:
The system dynamically changes the decomposition parameter (decomposition level) based on local image characteristics. Instead of maintaining a constant decomposition parameter across the entire image, the algorithm adjusts the parameter locally according to the complexity and frequency content of each region. This parameter adaptation enables the system to achieve better processing efficiency and detail preservation without requiring overly complex fixed schemes.
3Ease of operation
If equal computational resources are allocated to all image regions, then processing uniformity is maintained, but computational efficiency decreases and power consumption increases
Solution Approach 1:
The patent implements non-uniform computational resource allocation based on local image complexity. Regions with high-frequency content and fine details receive more computational resources including deeper decomposition and more processing iterations, while smooth low-frequency regions receive fewer resources. This local quality approach maintains processing uniformity in terms of quality output while optimizing computational efficiency by allocating resources according to actual need, thereby reducing overall power consumption.
Solution Approach 2:
The system applies partial processing to regions where full processing is unnecessary. Instead of applying maximum computational resources uniformly across the entire image, the algorithm identifies regions that require less processing (smooth areas with minimal detail) and applies reduced decomposition depth and processing intensity to those areas. This partial action principle maintains adequate processing quality where needed while significantly reducing computational power consumption in regions where it would be wasteful.
Data Source
AI summary
A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.