Nighttime Dehazing with a Synthetic Benchmark

Zhang, Jing; Cao, Yang; Zha, Zheng-Jun; Tao, Dacheng

doi:10.1145/3394171.3413763

Cited by 88 publications

(38 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When images are captured in the low-light and haze environment, it comes to the more challenging case, i.e., nighttime image dehazing. Similarly, some methods have been proposed based on either statistical priors or deep learning, e.g., maximum reflectance prior [153], glow separation [154], and ND-Net [155].…”

Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

Self Cite

466

168

View full text Add to dashboard Cite

In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

show abstract

Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

Self Cite

466

168

View full text Add to dashboard Cite

show abstract

“…Therefore, it is determined that utilizing the local estimate of atmospheric light may be a viable solution. In this context, the local estimate can be obtained using the novel maximum reflectance prior, as proposed by Zhang et al [ 56 , 57 ] for night-time image dehazing. However, because a more comprehensive investigation has to be done before discovering the exact reason, this failure in night-time scenes is left for future studies.…”

Section: Discussionmentioning

confidence: 99%

Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Ngo

Lee

Kang

2021

Sensors

View full text Add to dashboard Cite

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

show abstract

“…Yang et al 17 proposed a super-pixel-based algorithm for nighttime haze image dehazing. Zhang et al 18 proposed a synthetic method called 3R to simulate nighttime hazy images from daytime clear images and produce large-scale benchmark datasets. Ancuti et al 19 enhanced the visibility of nighttime haze images by local airlight estimation.…”

Section: Low-light Image Dehazingmentioning

confidence: 99%

“…Zhang et al 18 proposed a nighttime image dehazing benchmark to address the absence of large-scale benchmark datasets. The dataset contains Nighttime Hazy Cityscapes (NHC), Nighttime Hazy Middlebury (NHM), and Nighttime Hazy RESIDE (NHR).…”

Section: Training Datamentioning

confidence: 99%

AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

Chen

Sheng

et al. 2021

Computer Animation & Virtual

View full text Add to dashboard Cite

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively. K E Y W O R D S attention mechanism, image dehazing, low-light enhancement INTRODUCTIONHaze is common atmospheric phenomenon and often plagues the qualities of the images acquired by the cameras. While, at night with low light, the images further face severe degradations, including low visibility and intensity blurs. Recently, many efforts have been made to solve the problems of image dehazing and low-light image enhancement. However, existing methods dealt with the problems of image dehazing and low-light enhancement separately. These methods can achieve relatively good performance on corrupted images when removing a certain target type of distortion (i.e., haze or low-light), but they are not effective in removing both types of distortions.

show abstract

Nighttime Dehazing with a Synthetic Benchmark

Abstract: Figure 1: (a) Nighttime hazy images. (b) NDIM [41]. (c) GS [23]. (d) MRP [39]. (e) Our OSFD. (f) Our ND-Net.

Cited by 88 publications

References 39 publications

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

Contact Info

Product

Resources

About