2018/03/31 by Wenqi Ren, Lin Ma, Ren, Wenqi +12 · 62 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Context (archaeology) #Encoder #FOS: Computer and information sciences #Gamma correction #Image (mathematics) #Image Enhancement Techniques #Pattern recognition (psychology) #Pixel #Visibility #cs.CV
paper · pdf · doi:10.48550/arxiv.1804.00213
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/03/31 · openalex publication_date 2018/03/31 · arxiv updated 2018/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.