Vision Transformers for Single Image Dehazing

Song, Yuda; He, Zhiyong; Qian, Hui; Du, Xin

doi:10.48550/arxiv.2204.03883

Cited by 13 publications

(26 citation statements)

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“…They achieved excellent performance with limited parameters and proved that the proposed contrast loss function can bring further improvement to many previous networks. In 2022, Song et al proposed DehazeFormer [ 27 ] and successfully applied the Transformer, which has been successful in many visual fields, to the field of image dehazing. They modified many details of the Swin-Transformer [ 28 ] to make it more suitable for image dehazing tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The appearance of Transformer not only makes a breakthrough in advanced computer vision tasks, but also accelerates the performance improvement of image dehazing methods. As mentioned earlier, the DehazeFormer [ 27 ] scheme proposed by Song et al integrates Transformer into the image dehazing scheme and makes targeted modifications. Compared with a series of previous algorithms based on convolutional neural networks, it has made more objective improvements.…”

Section: Proposed Transformer–convolution Fusion Dehazing Networkmentioning

confidence: 99%

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An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

Xu¹,

Chen

Luo

et al. 2022

Sensors

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The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Proposed Transformer–convolution Fusion Dehazing Networkmentioning

confidence: 99%

An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

Xu¹,

Chen

Luo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

“…Recently, ViT-based dehazing [2,19] has made progress. DehazeFormer [19] is trained on synthetic fog images (RESIDE outdoor dataset [43]), which are not realistic and cause unreliable performance on real-world fog images. DeHamer [2] combines CNN and Transformer for image dehazing; however, memory and computation complexity slow down the convergence [20], causing inefficient performance on real-world high resolution fog images.…”

Section: Related Workmentioning

confidence: 99%

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Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal

Jin¹,

Yan²,

Yang³

et al. 2022

Preprint

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Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog's attenuation and airlight (or veiling effect) significantly weaken the background scene information in the input image. To address this problem, we introduce a structurerepresentation network with uncertainty feedback learning. Specifically, we extract the feature representations from a pre-trained Vision Transformer (DINO-ViT) module to recover the background information. To guide our network to focus on non-uniform fog areas, and then remove the fog accordingly, we introduce the uncertainty feedback learning, which produces uncertainty maps, that have higher uncertainty in denser fog regions, and can be regarded as an attention map that represents fog's density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying light colors that are possibly present in the input image. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively compared to the state-of-the-art methods in handling dense and non-uniform fog or smoke.

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Joint features-guided linear transformer and CNN for efficient image super-resolution

Wang,

Zhang,

Long

et al. 2024

Int. J. Mach. Learn. & Cyber.

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Vision Transformers for Single Image Dehazing

Cited by 13 publications

References 0 publications

An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network

Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal

Joint features-guided linear transformer and CNN for efficient image super-resolution

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