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Nighttime image dehazing is particularly challenging when dense haze and intense glow severely degrade or entirely obscure background information. Existing methods often struggle due to insufficient background priors and limited generative…

计算机视觉与模式识别 · 计算机科学 2025-11-21 Beibei Lin , Stephen Lin , Robby Tan

Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a…

计算机视觉与模式识别 · 计算机科学 2021-03-15 Binghan Li , Yindong Hua , Mi Lu

Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when applied to complex real-world hazy scenes, primarily due to…

计算机视觉与模式识别 · 计算机科学 2026-01-06 Chen Zhu , Huiwen Zhang , Yujie Li , Mu He , Xiaotian Qiao

Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily…

计算机视觉与模式识别 · 计算机科学 2022-07-26 Sai Mitheran , Anushri Suresh , Nisha J. S. , Varun P. Gopi

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents…

计算机视觉与模式识别 · 计算机科学 2022-11-16 Haoran Wei , Qingbo Wu , Hui Li , King Ngi Ngan , Hongliang Li , Fanman Meng , Linfeng Xu

We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image. Instead of relying on hand-crafted image priors or explicitly…

计算机视觉与模式识别 · 计算机科学 2018-05-10 Zheng Xu , Xitong Yang , Xue Li , Xiaoshuai Sun

This report presents the results of a sky detection technique used to improve the performance of a previously developed partial differential equation (PDE)-based hazy image enhancement algorithm. Additionally, a proposed alternative method…

计算机视觉与模式识别 · 计算机科学 2018-03-13 Uche A. Nnolim

Single image dehazing is a challenging ill-posed problem. Existing datasets for training deep learning-based methods can be generated by hand-crafted or synthetic schemes. However, the former often suffers from small scales, while the…

计算机视觉与模式识别 · 计算机科学 2024-01-17 Honglei Xu , Yan Shu , Shaohui Liu

This paper addresses the limitations of physical models in the current field of image dehazing by proposing an innovative dehazing network (CL2S). Building on the DM2F model, it identifies issues in its ablation experiments and replaces the…

计算机视觉与模式识别 · 计算机科学 2024-05-28 Yesian Rohn

Underwater images suffer from wavelength-dependent light absorption and scattering, which reduces visual quality. This phenomenon could limit the operational reliability of autonomous underwater vehicles, marine surveys, and offshore…

图像与视频处理 · 电气工程与系统科学 2026-05-14 Sahana Ray , Sanjay Ghosh

Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between…

计算机视觉与模式识别 · 计算机科学 2019-02-22 Ofir Nabati , David Mendlovic , Raja Giryes

This report presents the results of a multi-scale wavelet based scheme for single image de-hazing and underwater image enhancement. The scheme is fast and highly localized in addition to global enhancement of hazy images. A PDE-based…

计算机视觉与模式识别 · 计算机科学 2021-12-01 Uche A. Nnolim

This paper proposes a new technique based on nonlinear Adaptive Median filter (AMF) for image restoration. Image denoising is a common procedure in digital image processing aiming at the removal of noise, which may corrupt an image during…

计算机视觉与模式识别 · 计算机科学 2010-04-28 T. K. Thivakaran , RM. Chandrasekaran

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with…

计算机视觉与模式识别 · 计算机科学 2024-01-23 Yeying Jin , Beibei Lin , Wending Yan , Yuan Yuan , Wei Ye , Robby T. Tan

Nighttime images captured under hazy conditions suffer from severe quality degradation, including low visibility, color distortion, and reduced contrast, caused by the combined effects of atmospheric scattering, absorption by suspended…

计算机视觉与模式识别 · 计算机科学 2026-04-01 Francesco Moretti , Giulia Bianchi , Andrea Gallo

Image Dehazing aims to remove atmospheric fog or haze from an image. Although the Dehazing models have evolved a lot in recent years, few have precisely tackled the problem of High-Resolution hazy images. For this kind of image, the model…

计算机视觉与模式识别 · 计算机科学 2024-09-04 Bilel Benjdira , Anas M. Ali , Anis Koubaa

In image dehazing task, haze density is a key feature and affects the performance of dehazing methods. However, some of the existing methods lack a comparative image to measure densities, and others create intermediate results but lack the…

计算机视觉与模式识别 · 计算机科学 2024-04-30 Zhongze Wang , Haitao Zhao , Lujian Yao , Jingchao Peng , Kaijie Zhao

Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning…

计算机视觉与模式识别 · 计算机科学 2020-09-01 Bharath Raj N. , Venkateswaran N

Low-light hazy scenes commonly appear at dusk and early morning. The visual enhancement for low-light hazy images is an ill-posed problem. Even though numerous methods have been proposed for image dehazing and low-light enhancement…

计算机视觉与模式识别 · 计算机科学 2023-08-02 Chaoqun Zhuang , Yunfei Liu , Sijia Wen , Feng Lu

We present a method to remove, using only linear optics, exactly one photon from a field-mode. This is achieved by putting the system in contact with an absorbing environment which is under continuous monitoring. A feedback mechanism then…