Single UHD Image Dehazing Via Interpretable Pyramid Network

Xiao, Boxue; Zheng, Zhuoran; Chen, Xiang; Lv, Chen; Zhuang, Yunliang; Wang, Tao

doi:10.2139/ssrn.4134196

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Since the Laplacian pyramid was proposed in image processing, 23 it has been widely used in various image restoration tasks, such as image super-resolution, image dehazing, image translation, and style transfer. [24][25][26][27] The Laplacian pyramid is constructed by iteratively subtracting each level's upsampled version from the previous level to obtain high-frequency details. For example, Lai et al 24 proposed the Laplacian pyramid super-resolution network to reconstruct the subband residuals of high-resolution images progressively.…”

Section: Laplacian Pyramidmentioning

confidence: 99%

Low-light image enhancement via multistage Laplacian feature fusion

Liu,

Huang,

Zhang

et al. 2024

J. Electron. Imag.

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Low-light images inevitably suffer from degradation problems during the enhancement process, such as loss of detail and local overexposure or underexposure. Many existing methods target only one of these issues, leading to suboptimal results. We propose a multistage Laplacian feature fusion network (MLFFNet) capable of simultaneously mitigating both degradation difficulties. MLFFNet employs a pyramid framework that incrementally learns the degradation functions across various frequency bands, leveraging Laplacian feature maps at each stage.A key innovation of our approach is the supervised refinement module, which refines features through a dual strategy: an attention mechanism that enriches the detail capture, including edges, textures, and colors, and a residual mechanism that adjusts the luminance for a balanced exposure. The resultant enhanced image benefits from channel-wise attention, ensuring superior enhancement. Finally, the enhanced image is acquired by several channel attention blocks. Extensive experiments on various datasets indicate that our proposed MLFFNet outperforms the state-of-the-art methods both qualitatively and quantitatively.

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Section: Laplacian Pyramidmentioning

confidence: 99%

Low-light image enhancement via multistage Laplacian feature fusion

Liu,

Huang,

Zhang

et al. 2024

J. Electron. Imag.

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“…This method involves using both a traditional RGB hazy image and a transmission map, which is generated using a dark channel prior, as inputs for the network. A novel approach was introduced by (Xiao et al, 2022) to handle ultra-high-resolution (UHD) images for single image dehazing in real-time using a single GPU. The method is based on an infinite approximation of Taylor's theorem with the Laplace pyramid pattern, where low and high-order polynomials reconstruct image information.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method

Vishwakarma

Pillai

Punj

2023

Int. j. math. eng. manag. sci.

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Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between depth and the difference between the hazy image’s maximum and lowest color channels inspired the suggested study. Using a contrasting attention mechanism spanning sub-pixels and blocks, we offer a unique attention method to create high-quality, haze-free pictures. The L*a*b* color model has been proposed as an effective color space for dehazing images. A variational auto-encoder-based dehazing network may also be utilized for training since it compresses and attempts to reconstruct input images. Estimating hundreds of image-impacting characteristics may be necessary. In a variational auto-encoder, fuzzy input images are directly given a Gaussian probability distribution, and the variational auto-encoder estimates the distribution parameters. A quantitative and qualitative study of the RESIDE dataset will show the suggested method's accuracy and resilience. RESIDE’s subsets of synthetic and real-world single-image dehazing examples are utilized for training and assessment. Enhance the structural similarity index measure (SSIM) and peak signal-to-noise ratio metrics (PSNR).

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“…Adam optimizer is utilized to accelerate the training: Adam is generally considered quite robust to one's choice of hyper parameter values, so we kept many of the balanced default values provided by Tensor flow, and the default values of β 1 and β 2 are 0.9 and 0.999, respectively. e dehazing effectiveness and applicability are evaluated on testing images by being further compared with histogram equalization, Retinex, DCP, DehazeNet [27], AOD-NET [28], MSCNN [32], LapDehazeNet [33], and FCTF-Net [34]. e codes for all the above networks are derived from the download link provided in the corresponding papers or downloaded from 1 and 2.…”

Section: Comparative Experiments With Other State-of-e-artmentioning

confidence: 99%

“…However, ship detection under complex sea weather, such as sea fog, has not been studied. In this paper, the same network structure and experimental setup as [45] are used to verify the availability of the dehazing algorithm in ship detection.…”

Section: Application In Ship Detectionmentioning

confidence: 99%

“…Moreover, in order to obtain better multiscale features, a feature extraction module with a certain depth is designed to replace the convolution layer. Besides, an endto-end Laplacian pyramid defog network (LapDehazeNet) is proposed [33], in which Taylor's infinite approximation theorem is utilized to reconstruct low-frequency and highfrequency information of images by obtaining multiscale features through N branch networks of the pyramid network corresponding to N constraints in Taylor's theorem. e network uses Taylor's infinite approximation theorem to reconstruct the low-frequency information and high-frequency information of the image from a multiscale perspective by corresponding N constraint terms in Taylor's theorem through the N branch network of the pyramid network.…”

Section: Introductionmentioning

confidence: 99%

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Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

Liu

Baijun

2022

Mathematical Problems in Engineering

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Maritime video surveillance of visual perception system has become an essential method to guarantee unmanned surface vessels (USV) traffic safety and security in maritime applications. However, when visual data are collected in a foggy marine environment, the essential optical information is often hidden in the fog, potentially resulting in decreased accuracy of ship detection. Therefore, a dual-channel and two-stage dehazing network (DTDNet) is proposed to improve the clarity and quality of the image to guarantee reliable ship detection under foggy conditions. Specifically, an upper and lower sampling structure is introduced to expand the original two-stage dehazing network into a two-channel network, to further capture the image features from different scale. Meanwhile, the attention mechanism is combined to provide different weights for different feature maps to maintain more image information. Furthermore, the perceptual function is constructed with the MSE-based loss function, so that it can better reduce the gap between the dehazing image and the unhazy image. Extensive experiments show that DTDNet has a better dehazing performance on both visual effects and quantitative index than other state-of-the-art dehazing networks. Moreover, the dehazing network is combined with the problem of ship detection under a sea-fog environment, and experiment results demonstrate that our network can be effectively applied to improve the visual perception performance of USV.

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Single UHD Image Dehazing Via Interpretable Pyramid Network

Cited by 10 publications

References 7 publications

Low-light image enhancement via multistage Laplacian feature fusion

Low-light image enhancement via multistage Laplacian feature fusion

An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method

Dual-Channel and Two-Stage Dehazing Network for Promoting Ship Detection in Visual Perception System

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