UIEC^2-Net: CNN-based underwater image enhancement using two color space

Wang, Yudong; Guo, Jun; Gao, Huan; Yue, Huihui

doi:10.1016/j.image.2021.116250

Cited by 147 publications

(59 citation statements)

References 32 publications

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“…The above mentioned networks still reflect a considerable rate of failure due to lack of handling the saturation and contrast problems. In 23 , an end-to-end CNN with three blocks named RGB (for basic operations), HSV (saturation and luminance adjustment) and attention (stage for quality enhancement) is proposed where the final restored image is produced by a weighted sum between the RGB block output and the attention block’s RGB component as well as a weighted sum between HSV block output and the attention block’s HSV component. The input of the attention block is a concatenation of the raw image and the images of other two blocks (RGB block and HSV block).…”

Section: Introductionmentioning

confidence: 99%

Underwater image restoration with Haar wavelet transform and ensemble of triple correction algorithms using Bootstrap aggregation and random forests

Rowghanian

2022

Sci Rep

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This paper presents both a new strategy for traditional underwater image restoration using Haar wavelet transform as well as a new learned model that generates an ensemble of triple correction algorithm labels based on histogram quadrants’ cumulative distribution feature instead of generating pixel intensities. The Haar wavelet transform is our tentative dynamic stretching plan that is applied on the input image and its contrast stretched image to generate the degraded wavelet coefficients which are blended using Gaussian pyramid of the saliency weights to restore the original image. The ensemble of triple corrections exerts three color correction algorithms sequentially on the degraded image for restoration. The ensemble of algorithms entails the superposition effect of the red channel mean shifting, global RGB adaptation, global luminance adaptation, global saturation adaptation, luminance stretching, saturation stretching, contrast stretching, adaptive Gamma correction for red spectrum, even to odd middle intensity transference using look-up table, green to red spectrum transference using histogram equalization, local brightening, Dark Channel Prior, fusion restoration, and our Haar wavelet transform restoration. The source is available at https://github.com/vahidr213/Underwater-Image-Restoration-And-Enhancement-Collection.

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Section: Introductionmentioning

confidence: 99%

Underwater image restoration with Haar wavelet transform and ensemble of triple correction algorithms using Bootstrap aggregation and random forests

Rowghanian

2022

Sci Rep

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“…Based on this aspect, images were divided into low-light and shallow-water images, as shown in Figures 6a and 7a. To illustrate the effect of the proposed network on different types of underwater images better, inspired by [11], the test images were divided into five types: bluish underwater images, greenish underwater images, yellowish underwater images, low-illuminated underwater images, and shallow-water images, as shown in Figures 3-7, respectively. The ULAP mostly relied on underwater imaging models and prior knowledge, which made it less robust to complex scenes and even aggravated the effect of the color cast.…”

Section: Comparisons With Sota Methods On Full-reference Datasetsmentioning

confidence: 99%

“…Comparison Methods: WE-Net was compared with eight methods, including two traditional methods (UDCP [3] and ULAP [45]), a residual-network-based method (UResnet [8]), a shallow-network-based method (shallow-UWnet [9]), a color-balancebased method (UIEC2Net [11]), a physical model and CNN-fusion-based method (Chen et al [46]), a multi-stage method (Deep-WaveNet [10]), and a DWT-based method (Ma et al [17]).…”

Section: Implementation Detailsmentioning

confidence: 99%

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Multi-Level Wavelet-Based Network Embedded with Edge Enhancement Information for Underwater Image Enhancement

Sun

Meng

Tian

2022

JMSE

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As an image processing method, underwater image enhancement (UIE) plays an important role in the field of underwater resource detection and engineering research. Currently, the convolutional neural network (CNN)- and Transformer-based methods are the mainstream methods for UIE. However, CNNs usually use pooling to expand the receptive field, which may lead to information loss that is not conducive to feature extraction and analysis. At the same time, edge blurring can easily occur in enhanced images obtained by the existing methods. To address this issue, this paper proposes a framework that combines CNN and Transformer, employs the wavelet transform and inverse wavelet transform for encoding and decoding, and progressively embeds the edge information on the raw image in the encoding process. Specifically, first, features of the raw image and its edge detection image are extracted step by step using the convolution module and the residual dense attention module, respectively, to obtain mixed feature maps of different resolutions. Next, the residual structure Swin Transformer group is used to extract global features. Then, the resulting feature map and the encoder’s hybrid feature map are used for high-resolution feature map reconstruction by the decoder. The experimental results show that the proposed method can achieve an excellent effect in edge information protection and visual reconstruction of images. In addition, the effectiveness of each component of the proposed model is verified by ablation experiments.

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“…We use the SSIM loss [36] to impose the structure and texture similarity on the predicted image. In this paper, we use gray images, which convert from RGB images, to compute SSIM score, and for each pixel the SSIM value is computed within a 11 × 11 image patch around the pixel.…”

Section: Ssim Lossmentioning

confidence: 99%

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Shi

Wang

Zhou

et al. 2022

IET Image Processing

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Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

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UIEC^2-Net: CNN-based underwater image enhancement using two color space

Cited by 147 publications

References 32 publications

Underwater image restoration with Haar wavelet transform and ensemble of triple correction algorithms using Bootstrap aggregation and random forests

Underwater image restoration with Haar wavelet transform and ensemble of triple correction algorithms using Bootstrap aggregation and random forests

Multi-Level Wavelet-Based Network Embedded with Edge Enhancement Information for Underwater Image Enhancement

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

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