WaveDH: An Efficient Wavelet-Guided Convolutional Network for Single Image Dehazing

Core Concepts

WaveDH is a novel and compact convolutional network that leverages wavelet sub-bands for guided up-and-downsampling and frequency-aware feature refinement, achieving superior performance in single image dehazing with significantly reduced computational costs.

Abstract

The paper introduces WaveDH, a novel and efficient convolutional network for single image dehazing. The key contributions are:

Wavelet-guided up-and-downsampling blocks: WaveDH utilizes the inherent lossless and invertible properties of wavelet transform to perform optimized upsampling and downsampling, preserving high-frequency details while reducing computational costs.

Frequency-aware feature refinement block: WaveDH employs a coarse-to-fine feature refinement approach that strategically processes low-and-high frequency components, significantly improving representation learning efficiency.

Comprehensive experiments: Extensive experiments demonstrate that WaveDH outperforms many state-of-the-art dehazing methods on several benchmarks, while maintaining significantly lower model complexity.

The paper first provides an overview of the WaveDH architecture, which consists of wavelet-guided downsampling and upsampling blocks, as well as a frequency-aware feature refinement block. The downsampling block utilizes a novel squeeze-and-attention mechanism to optimize the feature downsampling process, while the upsampling block employs a dual-upsample and fusion mechanism to enhance high-frequency component awareness. The frequency-aware feature refinement block refines both low-and-high frequency information in a coarse-to-fine manner, ensuring comprehensive capture of scene details.
The authors conduct extensive ablation studies to quantify the contributions of the key components of WaveDH, including the dual-upsample and fusion mechanism, the wavelet attention module, and the convolution type used in the Fused-MBConv block. The results demonstrate the effectiveness of the proposed design choices in achieving a favorable trade-off between performance and computational efficiency.

Stats

The paper does not provide any specific numerical data or statistics in the main text. The focus is on the architectural design and the overall performance evaluation of the proposed WaveDH model.

Quotes

The paper does not contain any direct quotes that are particularly striking or support the key logics.

Key Insights Distilled From

WaveDH

by Seongmin Hwa... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01604.pdf

Deeper Inquiries

How can the wavelet-guided feature extraction and refinement approach in WaveDH be extended to other low-level vision tasks beyond image dehazing, such as image super-resolution or image restoration

The wavelet-guided feature extraction and refinement approach in WaveDH can be extended to other low-level vision tasks beyond image dehazing by adapting the methodology to suit the specific requirements of tasks like image super-resolution or image restoration. For image super-resolution, the wavelet decomposition can be utilized to extract multi-scale information from the input image, allowing for the reconstruction of high-resolution details. The low-frequency components can capture the global structure of the image, while the high-frequency components can enhance the finer details. By incorporating wavelet-guided upsampling and downsampling blocks, similar to those in WaveDH, the model can efficiently process the image at different scales, leading to improved super-resolution results. Additionally, for image restoration tasks, the frequency-aware feature refinement block in WaveDH can be leveraged to enhance the quality of the restored image by focusing on both low and high-frequency components. This approach can help in preserving important details while reducing noise and artifacts in the restored image.

What are the potential limitations of the wavelet transform-based approach, and how can they be addressed to further improve the efficiency-performance trade-off of WaveDH

One potential limitation of the wavelet transform-based approach in WaveDH could be the complexity of the wavelet decomposition process, which may introduce computational overhead. To address this limitation and further improve the efficiency-performance trade-off of WaveDH, several strategies can be implemented. Firstly, optimizing the wavelet transform implementation by utilizing efficient algorithms and data structures can help reduce the computational cost associated with the decomposition process. Additionally, exploring hybrid approaches that combine wavelet-based features with other efficient feature extraction methods, such as deep learning-based techniques like convolutional neural networks (CNNs), can help enhance the overall performance of the model. Moreover, incorporating adaptive mechanisms to dynamically adjust the level of wavelet decomposition based on the complexity of the input image can further optimize the processing efficiency of WaveDH. By continuously refining the balance between computational complexity and performance, WaveDH can achieve a more optimal trade-off in various low-level vision tasks.

Given the growing interest in vision transformers for image dehazing, how can the strengths of wavelet-based convolutional networks and transformer-based models be combined to develop even more powerful and efficient dehazing solutions

To leverage the strengths of both wavelet-based convolutional networks and transformer-based models for image dehazing, a hybrid architecture can be designed that combines the frequency-aware feature extraction capabilities of wavelet-based networks with the long-range dependency modeling of vision transformers. By integrating wavelet-guided feature extraction and refinement blocks within a transformer architecture, the model can effectively capture both local and global image features while maintaining computational efficiency. The wavelet-based blocks can handle the multi-scale information extraction and feature refinement, while the transformer components can focus on learning complex relationships and dependencies across the image. This hybrid approach can potentially enhance the dehazing performance by leveraging the complementary strengths of wavelet-based and transformer-based models, leading to more powerful and efficient dehazing solutions.