scholarly journals Deep Supervised Residual Dense Network for Underwater Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3289
Author(s):  
Yanling Han ◽  
Lihua Huang ◽  
Zhonghua Hong ◽  
Shouqi Cao ◽  
Yun Zhang ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Feature Learning ◽  
Vital Role ◽  
Dense Network ◽  
Low Contrast ◽  
Network Training ◽  
Underwater Image ◽  
Learning Capabilities ◽  
Color Restoration ◽  
High Level

Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.

scholarly journals Underwater Image Enhancement Using Improved CNN Based Defogging

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 150
Author(s):  
Meicheng Zheng ◽  
Weilin Luo
Keyword(s):  
Histogram Equalization ◽  
Input Image ◽  
Fusion Algorithm ◽  
Low Contrast ◽  
Network Training ◽  
Underwater Image ◽  
Comparison Results ◽  
Color Restoration ◽  
End To End ◽  
Lab Color Model

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.

scholarly journals Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

2021 ◽  
Vol 9 (2) ◽  
pp. 225
Author(s):  
Farong Gao ◽  
Kai Wang ◽  
Zhangyi Yang ◽  
Yejian Wang ◽  
Qizhong Zhang
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Fusion Method ◽  
Formation Model ◽  
Low Contrast ◽  
White Balance ◽  
Multi Scale ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

scholarly journals An approach for underwater image enhancement based on color correction and dehazing

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142096164
Author(s):  
Yue Zhang ◽  
Fuchun Yang ◽  
Weikai He
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Color Correction ◽  
Superior Performance ◽  
Local Contrast ◽  
Low Contrast ◽  
White Balance ◽  
Average Value ◽  
Underwater Image ◽  
Dark Channel

Due to the absorption and scattering effect on light when traveling in water, underwater images exhibit serious weakening such as color deviation, low contrast, and blurry details. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. To address these problems, a new approach for single underwater image enhancement based on fusion technology was proposed in this article. First, the original image is preprocessed by the white balance algorithm and dark channel prior dehazing technologies, respectively; then two input images were obtained by color correction and contrast enhancement; and finally, the enhanced image was obtained by utilizing the multiscale fusion strategy which is based on the weighted maps constructed by combining the features of global contrast, local contrast, saliency, and exposedness. Qualitative results revealed that the proposed approach significantly removed haze, corrected color deviation, and preserved image naturalness. For quantitative results, the test with 400 underwater images showed that the proposed approach produced a lower average value of mean square error and a higher average value of peak signal-to-noise ratio than the compared method. Moreover, the enhanced results obtain the highest average value in terms of underwater image quality measures among the comparable methods, illustrating that our approach achieves superior performance on different levels of distorted and hazy images.

scholarly journals Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

2021 ◽  
Vol 2066 (1) ◽  
pp. 012050
Author(s):  
Hao Chen ◽  
Hongsen He ◽  
Xinghua Feng
Keyword(s):  
Image Enhancement ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Underwater Environment ◽  
Underwater Image ◽  
Blue Channel ◽  
Dark Channel ◽  
Green Background ◽  
Enhancement Method

Abstract Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

scholarly journals An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5567 ◽  
Author(s):  
Yidan Liu ◽  
Huiping Xu ◽  
Dinghui Shang ◽  
Chen Li ◽  
Xiangqian Quan
Keyword(s):  
Image Enhancement ◽  
Deep Sea ◽  
Incident Light ◽  
Water Environment ◽  
Color Tone ◽  
Low Contrast ◽  
New Model ◽  
Underwater Image ◽  
Color Shift ◽  
Different Types

In the shallow-water environment, underwater images often present problems like color deviation and low contrast due to light absorption and scattering in the water body, but for deep-sea images, additional problems like uneven brightness and regional color shift can also exist, due to the use of chromatic and inhomogeneous artificial lighting devices. Since the latter situation is rarely studied in the field of underwater image enhancement, we propose a new model to include it in the analysis of underwater image degradation. Based on the theoretical study of the new model, a comprehensive method for enhancing underwater images under different illumination conditions is proposed in this paper. The proposed method is composed of two modules: color-tone correction and fusion-based descattering. In the first module, the regional or full-extent color deviation caused by different types of incident light is corrected via frequency-based color-tone estimation. And in the second module, the residual low contrast and pixel-wise color shift problems are handled by combining the descattering results under the assumption of different states of the image. The proposed method is experimented on laboratory and open-water images of different depths and illumination states. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms many other methods in enhancing the quality of different types of underwater images, and is especially effective in improving the color accuracy and information content in badly-illuminated regions of underwater images with non-uniform illumination, such as deep-sea images.

scholarly journals CSNN: An Augmented Spiking based Framework with Perceptron-Inception

2018 ◽  
Author(s):  
Qi Xu ◽  
Yu Qi ◽  
Hang Yu ◽  
Jiangrong Shen ◽  
Huajin Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Feature Learning ◽  
Classification Performance ◽  
Learning Ability ◽  
Extraction Ability ◽  
Feature Maps ◽  
Learning Capabilities ◽  
Biological Realism ◽  
High Level

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs.  The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.

scholarly journals Underwater image enhancement algorithm based on dark channel prior and underwater imaging model

2021 ◽  
Vol 336 ◽  
pp. 06033
Author(s):  
Zhengping Sun ◽  
Fubing Li ◽  
Yuying Yang
Keyword(s):  
Image Enhancement ◽  
Dark Channel Prior ◽  
Underwater Imaging ◽  
Low Contrast ◽  
Underwater Environment ◽  
Imaging Model ◽  
Underwater Image ◽  
Dark Channel ◽  
Color Compensation ◽  
Enhancement Method

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.

scholarly journals Underwater Image Enhancement Using Deep Residual Framework

2021 ◽  
pp. 52-56
Author(s):  
Prof. Anuja Phapale ◽  
Atal Deshmukh ◽  
Keshav Katkar ◽  
Onkar Karale ◽  
Puja Kasture
Keyword(s):  
Image Enhancement ◽  
Loss Function ◽  
Mean Squared Error ◽  
Super Resolution ◽  
Training Data ◽  
Neural Network Models ◽  
Low Contrast ◽  
Underwater Image ◽  
Training Mode ◽  
The Impact

There are various factors such as absorption, refraction & the phenomenon of scattering of light by particles suspended in water that are responsible for distorted colors, low contrast & blurred details of original underwater images. The traditional approaches include pre-processing the image using a descattering algorithm. The super-resolution (SR) method is applied. But this method has limitation that major part of the high frequency information is lost during descattering. This paper comes up with a solution for underwater image enhancement using a deep residual framework. Firstly, the generation of synthetic underwater images takes place for which cycle-consistent adversarial networks (CycleGAN) is employed. Further, these synthetic underwater images are used as training data for convolution neural network models. Secondly, the introduction of very-deep super-resolution reconstruction model to underwater resolution applications is carried out. Using this, the underwater Resnet model is proposed. It acts as a residual learning model for underwater image enhancement operations. Furthermore, the training mode & loss function are improved. Then, a multi-term loss function is formed which comprises of proposed edge difference loss & mean squared error loss. An asynchronous training mode is also being proposed that improves the performance of the multi-term loss function. Lastly, the discussion of the impact of batch normalization takes place. After a comparative analysis & underwater image enhancements, we can say that detailed enhancement performance & color correction of these proposed methods are much efficient & superior to that of previous traditional methods & deep learning models.

scholarly journals Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1220 ◽  
Author(s):  
Ho Sang Lee ◽  
Sang Whan Moon ◽  
Il Kyu Eom
Keyword(s):  
Image Enhancement ◽  
Correction Method ◽  
Color Correction ◽  
Water Medium ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
White Balance ◽  
Underwater Image ◽  
Corrected Image ◽  
Dark Channel

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

Exploring Coronal Structures with SOHO

2000 ◽  
Vol 179 ◽  
pp. 403-406
Author(s):  
M. Karovska ◽  
B. Wood ◽  
J. Chen ◽  
J. Cook ◽  
R. Howard
Keyword(s):  
Solar Corona ◽  
Image Enhancement ◽  
Coronal Mass Ejections ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Low Contrast ◽  
Contrast Structures ◽  
Scale Structures ◽  
Small Scale Structures ◽  
Coronal Structures

AbstractWe applied advanced image enhancement techniques to explore in detail the characteristics of the small-scale structures and/or the low contrast structures in several Coronal Mass Ejections (CMEs) observed by SOHO. We highlight here the results from our studies of the morphology and dynamical evolution of CME structures in the solar corona using two instruments on board SOHO: LASCO and EIT.

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