ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing

Susladkar, Onkar; Deshmukh, Gayatri; Nag, Subhrajit; Mantravadi, Ananya; Makwana, Dhruv; Ravichandran, Sujitha; R, Sai Chandra Teja; Chavhan, Gajanan H; Mohan, C. Krishna; Mittal, Sparsh

doi:10.1016/j.sysarc.2022.102736

Cited by 18 publications

(2 citation statements)

References 25 publications

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“…Figs 10 – 13 show the results of the ClarifyNet [ 40 ], FSDGN [ 41 ], MIRNetv2 [ 42 ], DehazeFormer [ 43 ], and UMFFA techniques on these two datasets, respectively. In all of the cases, the UMFFA technique produces the best outcomes.…”

Section: Experiments Resultsmentioning

confidence: 99%

Multi-channel feature fusion attention Dehazing network

Zou,

Xu,

2023

PLoS ONE

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Haze is a typical weather phenomena that has a significant negative impact on transportation safety, particularly in the port, highways, and airport runway areas. A multi-scale U-shaped dehazing network is proposed in this research, which is based on our multi-channel feature fusion attention structure. With the help of the feature fusion attention techniques, the model can focus on the intriguing locations with higher haze concentration area. In conjunction with UNet, it can achieve multi-scale feature reuse and residual learning, allowing it to fully utilize the feature information of each layer for image restoration. Experimental resulsts show that our technique performs well on a variety of test datasets. On highway data sets, the PSNR / SSIM / L∞ error performance over the novel technique is increased by 0.52% / 0.5% / 30.84%, 4.68% / 0.78% / 26.19% and 13.84% / 9.05% / 55.57% respectively, when compared to DehazeFormer, MIRNetv2, and FSDGN methods. The findings suggest that our proposed method performs better on image dehazing, especially in terms of L∞ error performance.

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Section: Experiments Resultsmentioning

confidence: 99%

Multi-channel feature fusion attention Dehazing network

Zou,

Xu,

2023

PLoS ONE

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“…An end-to-end method of self-guided image dehazing using progressive feature fusion, where the input hazy image was used as the guide image in the dehazing process, was presented in [ 9 ]. A deep image dehazing model based on convolutional neural networks was reported in [ 10 ]. In the model, ground truth images and high-pass and low-pass filtered images were used in the training phase, which involved fusion and attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm

Hsieh

Chen

Chang

2023

Sensors

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Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.

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Image denoising application in visual creation of art design based on intelligent algorithm

Xinong

2023

Soft Comput

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ClarifyNet: A high-pass and low-pass filtering based CNN for single image dehazing

Cited by 18 publications

References 25 publications

Multi-channel feature fusion attention Dehazing network

Multi-channel feature fusion attention Dehazing network

Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm

Image denoising application in visual creation of art design based on intelligent algorithm

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