Gated Fusion Network for Single Image Dehazing

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.

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• DehazeNet: An End-to-End System for Single Image Haze Removal
• Semantic Foggy Scene Understanding with Synthetic Data
• Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
• Densely Connected Pyramid Dehazing Network
• Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
• Benchmarking Single Image Dehazing and Beyond
• Deep Image Harmonization
• Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
• Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
• Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
• Cross-domain Human Parsing via Adversarial Feature and Label Adaptation
• Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution

Cited by in corpus (9)

• Densely Connected Pyramid Dehazing Network
• Gated Context Aggregation Network for Image Dehazing and Deraining
• Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
• Contrastive Learning for Compact Single Image Dehazing
• DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing
• A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning
• From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
• Image Demoireing with Learnable Bandpass Filters
• Implicit Euler ODE Networks for Single-Image Dehazing