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.

PERBANDINGAN METODE HOMOMORPHIC FILTERING DAN METODE CONTRAST STRECHING UNTUK PERBAIKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Adi Mora Lubis ◽  
Nelly Astuti Hasibuan ◽  
Imam Saputra
Keyword(s):  
Digital Image ◽  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through a specific software. In the water environment, the light factor greatly influences the results of the quality of the image obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. . uneven lighting and bluish tones. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of repairing digital images that experience interference in lighting. The lighting repair process uses homomorphic filtering and uses contrast striching and will compare the quality of both methods and test to prove the results of image quality between homomorphic filtering and contrast streching. Until later the results of both methods can be seen which is better. homomorphic filtering and contrast stretching can produce image improvements with pretty good performance.Keywords: Digital Image, Underwater Image, Homomorphic Filtering, Contrast Streching, Matlab R2010a

IMPLEMENTASI METODE RETINEX UNTUK MENINGKATKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Bainun Harahap
Keyword(s):  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Object Search ◽  
Image Improvement ◽  
Underwater Object

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through certain software devices. In the water environment, light factors greatly influence the results of image quality obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. Underwater imagery is widely used as an object in various activities such as underwater habitat mapping, underwater environment monitoring, underwater object search. Uneven lighting and colors that tend to be bluish and runny. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of improvement in digital images that experience interference in lighting and exposure to sunlight. The lighting repair process uses the retinex method and will compare the quality of the two methods later. Until later the results of both methods can be seen which is better. Retinex method can produce image improvement with high performance.Keywords: Digital Cintra, Underwater, Matlab Retinex Method

scholarly journals Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

2021 ◽  
Vol 9 (6) ◽  
pp. 570
Author(s):  
Qingliang Jiao ◽  
Ming Liu ◽  
Pengyu Li ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Image Restoration ◽  
Qualitative And Quantitative Analysis ◽  
Formation Model ◽  
High Quality ◽  
Thermal Exchange ◽  
Qualitative And Quantitative ◽  
Smooth Variation ◽  
Underwater Image ◽  
Dark Channel

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

Defocus blurred image restoration by minimizing second-order central moment

2011 ◽  
Author(s):  
Sheng Zhong ◽  
Mingzhi Jin ◽  
Luxin Yan ◽  
Tianxu Zhang
Keyword(s):  
Image Restoration ◽  
Central Moment ◽  
Second Order ◽  
Blurred Image

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.

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