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 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 A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3954 ◽  
Author(s):  
Qingqing Fu ◽  
Zhengbing Zhang ◽  
Mehmet Celenk ◽  
Aiping Wu
Keyword(s):  
Contrast Enhancement ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Input Image ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Low Contrast ◽  
Perceptual Evaluation ◽  
Limit Value ◽  
Enhancement Method

Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functions of the POSHE and build a new quality evaluation index considering the effects of the mean gradient and mean structural similarity. The new index is designed to obtain the optimal clip-limit value for histogram equalization of the sub-block. It makes the choice of the optimal clip-limit automatically according to the input image. Experimental results based on visual perceptual evaluation and quantitative measures demonstrate that the proposed method yields better quality in terms of enhancing the contrast, emphasizing the local details while preserving the brightness and restricting the excessive enhancement compared with the other seven histogram equalization-based techniques from the literature. This study provides a feasible and effective method to enhance ultrasonic logging images obtained through piezoceramic transducers and is significant for the interpretation of actual ultrasonic logging data.

Sequential Model for Digital Image Contrast Enhancement

2020 ◽  
Vol 13 ◽  
Author(s):  
Monika Agarwal ◽  
Geeta Rani ◽  
Shilpy Agarwal ◽  
Vijaypal Singh Dhaka
Keyword(s):  
Contrast Enhancement ◽  
Maximum Entropy ◽  
Histogram Equalization ◽  
Input Image ◽  
Maximum Brightness ◽  
Low Contrast ◽  
Entropy Maximization ◽  
Image Contrast Enhancement ◽  
Constrained Model ◽  
Minimum Noise

Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.

scholarly journals Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jingchun Zhou ◽  
Dehuan Zhang ◽  
Weishi Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Gradient Field ◽  
Low Contrast ◽  
Frequency Information ◽  
Underwater Image ◽  
Enhancement Method ◽  
Quantitative Performance ◽  
Color Cast

To solve the color cast and low contrast of underwater images caused by the effects of light absorption and scattering, we propose a novel underwater image enhancement method via bi-interval histogram equalization. The proposed method consists of three main parts: color correction, contrast enhancement, and multiscale fusion. First, the color cast is eliminated by automatic white balancing. Then, homomorphic filtering is adopted to decompose the image into high-frequency information and low-frequency information, the high-frequency information is enhanced by the gradient field bi-interval equalization which enhances the contrast and details of the image, and the low-frequency information is disposed via gamma correction for adjusting the exposure. Finally, we adopt a multiscale fusion strategy to fuse the high-frequency information, high-frequency after bi-interval equalization, and low-frequency information based on contrast, saturation, and exposure. Qualitative and quantitative performance evaluations demonstrate that the proposed method can effectively enhance the details and global contrast of the image and achieve better exposedness of the dark areas, which outperforms several state-of-the-art methods.

scholarly journals Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

2017 ◽  
Author(s):  
JINXIANG MA ◽  
Xinnan Fan ◽  
Simon X. Yang ◽  
Xuewu Zhang ◽  
Xifang Zhu
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Histogram Equalization ◽  
Color Spaces ◽  
Edge Detector ◽  
Fusion Algorithm ◽  
Adaptive Histogram Equalization ◽  
Underwater Image ◽  
Rgb Images ◽  
Three Components

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Adaptive Stretching Method for Underwater Image Color Correction

2017 ◽  
Vol 32 (02) ◽  
pp. 1854001 ◽  
Author(s):  
Jun Ao ◽  
Chunbo Ma
Keyword(s):  
Performance Analysis ◽  
Water Channel ◽  
Computational Cost ◽  
Histogram Equalization ◽  
Subjective Impression ◽  
Low Contrast ◽  
Underwater Image ◽  
Color Spectrum ◽  
Color Cast ◽  
Image Performance

The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computational cost low at the same time. To achieve adaptability, the adaptable threshold is deduced from the histogram of image. Performance analysis reveals that the proposed method significantly enhances the image contrast, reduces the color cast and meanwhile, keeps the computational consumption low.

scholarly journals Enhancement Underwater Image Using Histogram Equalization Based on Color Restoration

2019 ◽  
Vol 14 (2) ◽  
pp. 641-647
Author(s):  
Zahraa S. Abd-Al Ameer ◽  
Hazim G. Daway ◽  
Hana H. Kareem
Keyword(s):  
Histogram Equalization ◽  
Underwater Image ◽  
Color Restoration

scholarly journals Dual Filter based Images Fusion Algorithm for CT and MRI Medical Images

2019 ◽  
Vol 8 (9) ◽  
pp. 2673-2678
Keyword(s):  
Wavelet Transform ◽  
Medical Images ◽  
Input Image ◽  
Fusion Algorithm ◽  
Comparison Results ◽  
Fused Image ◽  
Laplacian Filter ◽  
Magnetic Resonance Imaging Mri ◽  
Approximation Coefficients ◽  
Ct And Mri

A novel image fusion algorithm based on two filters, one is laplacian filter for de-nosing the detailed coefficients and second filter is Guided Filter (GF) used to refine the approximation as well as detailed coefficient for Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) medical images is proposed. Because of using wavelet transform, we obtained approximation coefficient and other three coefficients of CT and MRI images. Now two weight maps are obtained after the process of denoising. Another reason for obtaining two weight maps is because of comparison. Here comparison is done between two approximation coefficient and six detailed coefficients. By using the approximation coefficients and detailed coefficients, GF is designed. Here GF will guide an image corresponding to the weight maps. Here the weight maps are smoothed using GF and this is mainly served as input image. Hence the weighted fusion algorithm will fuse the both CT and MRI images. A pure fused image is obtained only when the CT and MRI images are refined by inverse wavelet transform. From the comparison results, it can observe that the proposed system gives better results compared to existing system. As well as the proposed system will give maximum amount of input in detail manner

INTENSITY ENHANCEMENT ON OUTDOOR IMAGES

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Yaseen Al-Zubaidy ◽  
Rosalina Abdul Salam ◽  
Khairi Abdulrahim
Keyword(s):  
Weather Conditions ◽  
Histogram Equalization ◽  
Input Image ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Saturation Intensity ◽  
Wide Range ◽  
Bad Weather ◽  
Atmospheric Phenomena ◽  
Different Levels

Outdoor images that are captured in bad weather conditions have low contrast and infidelity colours. Under the turbid medium conditions such as haze, mist, fog and drizzle, the light which reaches to the sensor is attenuated by atmospheric particles. These atmospheric phenomena degrade the contrast intensity of outdoor images based on haze density. In this research, we present new method to improve both the intensity and fine details of outdoor scene images. The RGB (Red, Green and Blue) input image is converted to the HSI (Hue Saturation Intensity) colour space and the density of the haze is estimated. Then, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the degraded intensity based on the estimation of the density of the haze. Our method is effective in a wide range of weather conditions and under different levels of visibility.

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.

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