Underwater Image Refinement Using Color Cast and Wavelet Transform

2021 ◽  
Author(s):  
Shubhangi N Ghate ◽  
Dr.Mangesh D Nikose
Keyword(s):  
Wavelet Transform ◽  
Underwater Image ◽  
Color Cast

scholarly journals Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images

2020 ◽  
Vol 10 (18) ◽  
pp. 6392
Author(s):  
Xieliu Yang ◽  
Chenyu Yin ◽  
Ziyu Zhang ◽  
Yupeng Li ◽  
Wenfeng Liang ◽  
...  
Keyword(s):  
Color Space ◽  
Color Correction ◽  
Test Methods ◽  
Chromatic Adaptation ◽  
Water Type ◽  
Underwater Image ◽  
Other Information ◽  
Color Restoration ◽  
Color Cast ◽  
Imaging Conditions

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.

Processing of Sonar Image Based on Compressive Sensing

2011 ◽  
Vol 301-303 ◽  
pp. 719-723 ◽  
Author(s):  
Zhi Jing Xu ◽  
Huan Lei Dai ◽  
Pei Pei Cao
Keyword(s):  
Wavelet Transform ◽  
Matching Pursuit ◽  
Experimental Result ◽  
Underwater Acoustic Channel ◽  
Sonar Image ◽  
Underwater Image ◽  
Reconstruction Image ◽  
Observation Matrix ◽  
Efficient Transmission

The particularity of the underwater acoustic channel has put forward a higher request for collection and efficient transmission of the underwater image. In this paper, based on the characteristics of sonar image, wavelet transform is used to sparse decompose the image, and selecting Gaussian random matrix as the observation matrix and using the orthogonal matching pursuit (OMP) algorithm to reconstruct the image. The experimental result shows that the quality of the reconstruction image and PSNR have gained great ascension compared to the traditional compression and processing of image based on the wavelet transform while they have the same measurement numbers in the coding portion. It provides a convenient for the sonar image’s underwater transmission.

Leveraging Deep Statistics for Underwater Image Enhancement

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-20
Author(s):  
Yang Wang ◽  
Yang Cao ◽  
Jing Zhang ◽  
Feng Wu ◽  
Zheng-Jun Zha
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Light Propagation ◽  
Synthesis Method ◽  
Color Correction ◽  
Training Data ◽  
Depth Of Field ◽  
Underwater Imaging ◽  
Underwater Image ◽  
Color Cast

Underwater imaging often suffers from color cast and contrast degradation due to range-dependent medium absorption and light scattering. Introducing image statistics as prior has been proved to be an effective solution for underwater image enhancement. However, relative to the modal divergence of light propagation and underwater scenery, the existing methods are limited in representing the inherent statistics of underwater images resulting in color artifacts and haze residuals. To address this problem, this article proposes a convolutional neural network (CNN)-based framework to learn hierarchical statistical features related to color cast and contrast degradation and to leverage them for underwater image enhancement. Specifically, a pixel disruption strategy is first proposed to suppress intrinsic colors’ influence and facilitate modeling a unified statistical representation of underwater image. Then, considering the local variation of depth of field, two parallel sub-networks: Color Correction Network (CC-Net) and Contrast Enhancement Network (CE-Net) are presented. The CC-Net and CE-Net can generate pixel-wise color cast and transmission map and achieve spatial-varied color correction and contrast enhancement. Moreover, to address the issue of insufficient training data, an imaging model-based synthesis method that incorporates pixel disruption strategy is presented to generate underwater patches with global degradation consistency. Quantitative and subjective evaluations demonstrate that our proposed method achieves state-of-the-art performance.

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.

A Novel Adaptive Underwater Image Biorthogonal Basic Wavelet Transform De-Noising Approach

2013 ◽  
Vol 411-414 ◽  
pp. 1335-1340 ◽  
Author(s):  
Ming Wei Sheng ◽  
Yong Jie Pang ◽  
Hai Huang ◽  
Tie Dong Zhang
Keyword(s):  
Wavelet Transform ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Image Process ◽  
Video Cameras ◽  
Underwater Image ◽  
Basic Wavelet ◽  
Filter Methods ◽  
Blurred Image ◽  
Contrast Image

AUVs are usually equipped with video cameras to obtain the environment underwater information. Underwater images often suffer from effects such as diffusion, scatter and caustics. In order to improve the image quality and contrast, image restoration is need to be carried out before other image process. In this paper, a novel adaptive de-noising algorithm based on multi-wavelet transform was proposed in order to remove the Gaussian noise from the blurred underwater image. Firstly, the Gaussian noise deterioration of the image model was given. Secondly, the wavelet transform algorithm using Biorthogonal as a basic wavelet for underwater image decomposition and reconstruction was presented. Finally, Haar and Biorthogonal basic wavelet were chosen separately for adaptive de-noising algorithm for the blurred image restoration. By contrast with other filter methods, the experiment results verified its useful behaviors, and demonstrate that the raised de-noising approach can achieve fairly desired de-noising effectiveness for underwater image.

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