Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images (1904.02904v1)

Abstract: Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been recorded by introducing real haze, generated by professional haze machines. The hazy and haze-free corresponding scenes contain the same visual content captured under the same illumination parameters. Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes. We also provide a comprehensive qualitative and quantitative evaluation of state-of-the-art single image dehazing techniques based on the Dense-Haze dataset. Not surprisingly, our study reveals that the existing dehazing techniques perform poorly for dense homogeneous hazy scenes and that there is still much room for improvement.

• The paper introduces the Dense-Haze dataset featuring 33 dense hazy and haze-free image pairs that address existing gaps in dehazing validation.
• The methodology leverages professional haze machines under controlled cloudy and low-wind conditions to ensure uniform lighting across image pairs.
• Evaluation using PSNR, SSIM, and CIEDE2000 metrics reveals that state-of-the-art dehazing algorithms struggle with dense haze, highlighting a need for more robust techniques.

Dense-Haze: Advancing the Benchmarking of Single Image Dehazing

The paper presents a critical contribution to the domain of single image dehazing through the introduction of the Dense-Haze dataset. This dataset addresses the notable gap in the validation of dehazing methods due to the absence of comprehensive and realistic hazy and haze-free image pairs. The authors have meticulously recorded 33 pairs of dense, hazy scenes alongside their haze-free counterparts, using professional haze machines to create authentic atmospheric conditions. The work emphasizes the inadequacy of current dehazing techniques when subjected to dense haze scenarios, revealing significant room for enhancement in this problem space.

Methodological Considerations

The generation of the Dense-Haze dataset involves several innovative methodological approaches. Utilizing a pair of high-fidelity professional haze machines, the authors achieve a consistent and dense hazy environment. They counter typical recording challenges such as illumination variance and environmental stability by recording during specific atmospheric conditions, specifically in cloudy, low-wind scenarios. This ensures the uniformity of lighting conditions across the hazy and non-hazy image pairs. The effort expended to ensure such conditions underscores the dataset's potential as a standard for future dehazing research.

Evaluation of State-of-the-Art Methods

The paper offers a comprehensive evaluation of several leading single-image dehazing methodologies, ranging from heuristic approaches like Dark Channel Prior (DCP) to machine learning models including CNN-based strategies. The authors evaluate these techniques using standard quantitative metrics such as PSNR, SSIM, and CIEDE2000, demonstrating that current methodologies generally yield suboptimal results when applied to the challenging dense haze scenes of the Dense-Haze dataset. This quantitative analysis, supported by qualitative assessments, provides a robust benchmark against which future dehazing algorithms can be tested.

Implications and Future Directions

The introduction of the Dense-Haze dataset holds significant implications for both the theoretical development and practical applications of image dehazing. The authors’ findings underscore the importance of developing more sophisticated models capable of addressing the challenges posed by dense haze conditions, which are ubiquitous in real-world applications like autonomous driving and outdoor surveillance systems. Given the poor performance of existing methods, further research could focus on enhancing transmission map estimation, exploring more advanced machine learning architectures, and incorporating domain-specific knowledge into dehazing models.

Theoretical contributions from studies using the Dense-Haze dataset could also lead to more generalized solutions in computer vision applications dealing with adverse environmental conditions. The dataset encourages the development of new benchmarks and evaluation metrics that better encapsulate the nuances of dehazing under dense atmospheric conditions.

In conclusion, the Dense-Haze dataset serves as a pivotal resource in pushing the boundaries of single-image dehazing research. The work highlights the deficiencies in current modeling approaches when facing dense haze, stressing the need for continued innovation in this field to achieve practical, reliable solutions. Future research will undoubtedly benefit from the insights and evaluation standards established by this dataset, paving the way towards more effective dehazing techniques.