Gated Fusion Network for Single Image Dehazing (1804.00213v1)

Abstract: In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms.

• The paper presents a gated fusion network that bypasses traditional transmission estimation to effectively restore hazy images.
• It employs a multi-scale encoder-decoder architecture that integrates white balance, contrast enhancement, and gamma correction for optimal visibility.
• Quantitative evaluations reveal higher PSNR and SSIM scores against state-of-the-art techniques on both synthetic and real-world datasets.

Gated Fusion Network for Single Image Dehazing: An Analytical Overview

The paper "Gated Fusion Network for Single Image Dehazing" introduces a new approach to the image dehazing problem, leveraging a Gated Fusion Network (GFN) that avoids traditional transmission and atmospheric light estimation. This method aims to restore a clear image from a hazy input using deep learning techniques that enhance visibility without relying on hand-crafted features or assumptions.

Core Methodology

The proposed GFN is an end-to-end trainable neural network comprising an encoder and a decoder. It adopts a unique fusion-based strategy to process three derived inputs from the original hazy image through White Balance (WB), Contrast Enhancement (CE), and Gamma Correction (GC). These three processes are designed to tackle two critical components of hazy images: color distortion from atmospheric light and visibility degradation from light scattering.

The fusion network computes pixel-wise confidence maps that effectively combine information from each input, preserving the regions with optimal visibility. This novel approach circumvents the traditional challenge of accurately estimating scene transmission and atmospheric light variables.

Network Architecture

The network employs a multi-scale framework to address potential halo artifacts that are common in image dehazing tasks. By handling image resolutions from coarse to fine scales, the method mitigates these artifacts while maintaining detail fidelity. The dilation network enhances the receptive fields, expanding contextual information without losing localized details.

An adversarial loss component is incorporated into the training process to encourage the network to produce realistic and artifact-free images. This addition is expected to improve the resulting image quality, ensuring clarity and consistency across diverse hazy conditions.

Numerical Results and Comparisons

The GFN was rigorously evaluated against existing state-of-the-art algorithms, including both priors-based methods and CNN-based approaches. Quantitative assessments on synthetic datasets revealed that the GFN consistently achieved PSNR and SSIM values superior to traditional techniques. This is indicative of its robust ability to address varying haze concentrations effectively.

In real-world scenarios, GFN-produced images demonstrated superior visual quality, free from the color distortions and residual haze often observed in outputs from other methods. These results underscore the practical utility of the GFN in processing real-world images.

Conclusions and Future Outlook

The introduction of the Gated Fusion Network presents substantial advancements in image dehazing methodologies. By bypassing transmission estimation, the GFN reduces dependency on physical models, improving generalizability and efficacy.

While current results are promising, further exploration may focus on integrating adaptive mechanisms for more nuanced dehazing across diverse environmental conditions. Future work could also explore unsupervised learning paradigms to further enhance adaptability and performance across unstructured and variable datasets.

The paper's contributions lie in advancing both theoretical and practical applications of image dehazing, offering a flexible and powerful tool for clear image restoration that holds promise across numerous fields requiring accurate visual data interpretation.