Results 41 to 50 of about 4,947 (184)
Multiscale Image Dehazing Method Combining Sky Segmentation and Pyramid Fusion [PDF]
Jisuanji gongcheng, 2023 A multiscale image dehazing method that combines sky segmentation and pyramid fusion is proposed to address contrast degradation, dark tones, and overexposure in traditional image dehazing methods.
Xinjie XIAO, Zhiwei LI, Nannan ZHANG, Yuqing SUN, Wuneng ZHOU
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AED-Net: A Single Image Dehazing
IEEE Access, 2022 In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases.
Sargis A. Hovhannisyan +3 more
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Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
, 2020 Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Das, Sourya Dipta, Dutta, Saikat
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Vision Transformers for Single Image Dehazing
IEEE Transactions on Image Processing, 2023 Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, convolutional neural network-based methods have dominated image dehazing. However, vision Transformers, which has recently made a breakthrough in high-level vision tasks, has not brought new dimensions to image dehazing. We
Yuda Song, Zhuqing He, Hui Qian, Xin Du
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Image dehazing based on double branch convolution and detail enhancement
Xibei Gongye Daxue XuebaoBecause of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module ...
ZHAI Fengwen, ZHU Yutong, JIN Jing
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Learning of Image Dehazing Models for Segmentation Tasks
, 2019 To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth.
berman +9 more
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Single Image Dehazing Using End-to-End Deep-Dehaze Network [PDF]
Electronics, 2020 Haze is a natural distortion to the real-life images due to the specific weather conditions. This distortion limits the perceptual fidelity, as well as information integrity, of a given image. Image dehazing for the observed images is a complicated task because of its ill-posed nature.
Masud An-Nur Islam Fahim, Ho Yub Jung
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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
, 2018 This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes.
A Bar Hillel +20 more
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Nighttime Image Dehazing Based on Point Light Sources
Applied Sciences, 2022 Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks.
Xin-Wei Yao +4 more
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, 2018 Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage
Barnard Kobus +6 more
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