Abstract
Hazy images degrade visual quality, and dehazing is a crucial prerequisite for subsequent processing tasks. Most current dehazing methods rely on neural networks and face challenges such as high computational parameter pressure and weak generalization capabilities. This paper introduces PriorNet--a novel, lightweight, and highly applicable dehazing network designed to significantly improve the clarity and visual quality of hazy images while avoiding excessive detail extraction issues. The core of PriorNet is the original Multi-Dimensional Interactive Attention (MIA) mechanism, which effectively captures a wide range of haze characteristics, substantially reducing the computational load and generalization difficulties associated with complex systems. By utilizing a uniform convolutional kernel size and incorporating skip connections, we have streamlined the feature extraction process. Simplifying the number of layers and architecture not only enhances dehazing efficiency but also facilitates easier deployment on edge devices. Extensive testing across multiple datasets has demonstrated PriorNet's exceptional performance in dehazing and clarity restoration, maintaining image detail and color fidelity in single-image dehazing tasks. Notably, with a model size of just 18Kb, PriorNet showcases superior dehazing generalization capabilities compared to other methods. Our research makes a significant contribution to advancing image dehazing technology, providing new perspectives and tools for the field and related domains, particularly emphasizing the importance of improving universality and deployability.
Abstract (translated)
模糊图像降低视觉质量，而去雾是后续处理任务的關鍵先決條件。目前的大多数去雾方法依賴於神經網絡，並面臨高計算參數壓力和弱泛化能力的挑戰。本文介紹了 PriorNet--一個新輕量級且高度適用的去霧網絡，旨在顯著改善模糊圖像的清晰度和視覺質量，同時避免過度細節提取問題。PriorNet 的核心是原始的多維交互注意力（MIA）機制，有效捕捉廣泛的灰霾特性，大幅降低複雜系統的計算負荷和泛化困難。通過使用相同的卷積内核大小並包含跳躍連接，我們簡化了特徵提取過程。通過在多個數據集上的測試，PriorNet 在去霧和清晰度恢復方面的表現非常出色，保持單一圖像去霧任務中的圖像細節和色彩保真度。值得注意的是，PriorNet 的模型大小僅為 18Kb，其在去霧擴展能力上比其他方法优越。我們的研究在推動圖像去霧技術發展方面做出了重要的貢獻，為該領域和相關領域提供了新的視角和工具，尤其強調了提高普遍性和部署能力的必要性。
URL
https://arxiv.org/abs/2404.15638
