Enhanced Pix2pix Dehazing Network

Qu, Yanyun; Chen, Yizi; Huang, Jingying; Xie, Yuan

doi:10.1109/cvpr.2019.00835

Cited by 576 publications

(383 citation statements)

References 17 publications

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“…As a public benchmark for image dehazing and beyond, the sub-dataset SOTS [ 32 ] in RESIDE containing 500 indoor images and 500 outdoor images with different haze concentration was used for testing the performance of different dehazing algorithms. The quantitative results of our model and the extra seven state-of-the-art methods tested on SOTS are displayed in Table 4 and Table 5 , where the quantitative values of some methods were collected from [ 20 , 21 , 23 ]. From Table 4 , we can see that our model ranked the third among popular dehazing methods on the indoor images of SOTS, only second to the results by GridDN [ 23 ] and EPDN [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

“…The quantitative results of our model and the extra seven state-of-the-art methods tested on SOTS are displayed in Table 4 and Table 5 , where the quantitative values of some methods were collected from [ 20 , 21 , 23 ]. From Table 4 , we can see that our model ranked the third among popular dehazing methods on the indoor images of SOTS, only second to the results by GridDN [ 23 ] and EPDN [ 20 ]. Meanwhile, our model ranked the second on outdoor images of SOTS as shown in Table 5 .…”

Section: Resultsmentioning

confidence: 99%

“…However, the design of AODN also led to poor accuracy. From the last row of Table 9 , we can see that our method outperformed AODN and most dehazing methods, e.g., DCPDN, PWAB, AMEF and GPE, on outdoor images of the SOST dataset in terms of PSNR by up to 4.25 dB at least, only second to PIDN [ 20 ]. Hence, our method was much more efficient in comparison (the run time was reduced to 0.021 s) and could produce better results with fewer parameters (the number of parameters was 1,534,799, only 6% of that used by DCPDN) than these state-of-the-art methods.…”

Section: Resultsmentioning

confidence: 99%

“…Later, Zhang et al proposed a densely connected pyramid dehazing network (DCPDN), which jointly learns the transmission map, atmospheric light and dehazing result by the pyramid densely connected network (PDCN), U network and generative adversarial network (GAN). Qu et al proposed the enhanced pix2pix dehazing network (EPDN), motivated by the success of the generative adversarial network (GAN) [ 20 ]. Dudhane et al further presented a generative adversarial networks with residual inception module (RIGAN) to remove haze [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Residual Dense Pyramid Network for Image Dehazing

Yin

Wang

Yang

2019

Entropy

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Recently, convolutional neural network (CNN) based on the encoder-decoder structurehave been successfully applied to image dehazing. However, these CNN based dehazing methodshave two limitations: First, these dehazing models are large in size with enormous parameters, whichnot only consumes much GPU memory, but also is hard to train from scratch. Second, these models,which ignore the structural information at different resolutions of intermediate layers, cannot captureinformative texture and edge information for dehazing by stacking more layers. In this paper, wepropose a light-weight end-to-end network named the residual dense pyramid network (RDPN)to address the above problems. To exploit the structural information at different resolutions ofintermediate layers fully, a new residual dense pyramid (RDP) is proposed as a building block.By introducing a dense information fusion layer and the residual learning module, the RDP canmaximize the information flow and extract local features. Furthermore, the RDP further learnsthe structural information from intermediate layers via a multiscale pyramid fusion mechanism.To reduce the number of network parameters and to ease the training process, we use one RDPin the encoder and two RDPs in the decoder, following a multilevel pyramid pooling layer forincorporating global context features before estimating the final result. The extensive experimentalresults on a synthetic dataset and real-world images demonstrate that the new RDPN achievesfavourable performance compared with some state-of-the-art methods, e.g., the recent denselyconnected pyramid dehazing network, the all-in-one dehazing network, the enhanced pix2pixdehazing network, pixel-based alpha blending, artificial multi-exposure image fusions and thegenetic programming estimator, in terms of accuracy, run time and number of parameters. To bespecific, RDPN outperforms all of the above methods in terms of PSNR by at least 4.25 dB. The runtime of the proposed method is 0.021 s, and the number of parameters is 1,534,799, only 6% of thatused by the densely connected pyramid dehazing network.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Residual Dense Pyramid Network for Image Dehazing

Yin

Wang

Yang

2019

Entropy

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“…Zhang et al also addressed the end-to-end dehazing problem under a deep learning based densely connected pyramid dehazing network [ 34 ]. Approaches using generative adversarial networks have also been studied in recent years [ 37 , 38 , 39 , 40 ] Although these methods are capable of recovering satisfying results in daytime, their performances on nighttime dehazing are quite limited. In addition, learning based methods, especially the data-driven deep learning based approaches rely on the sufficient training data.…”

Section: Related Workmentioning

confidence: 99%

SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images

Guo

Shi

2020

Sensors

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Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.

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

Chen

Sheng

et al. 2021

Computer Animation & Virtual

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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.

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Enhanced Pix2pix Dehazing Network

Cited by 576 publications

References 17 publications

A Novel Residual Dense Pyramid Network for Image Dehazing

A Novel Residual Dense Pyramid Network for Image Dehazing

SIDE—A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images

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

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