A novel underwater sonar image enhancement algorithm based on approximation spaces of random sets

Underwater environment is complex and random. The images obtained from underwater by sonar always have uneven background gray distribution and fuzzy details of boundary. Hence the low-quality sonar images need to be enhanced before analysis. This paper presents a sonar image enhancement algorithm based on the approximation spaces of random sets. First of all, the knowledge representation of the underwater image is constructed by the approximation spaces of random sets. According to the background knowledge, the image is divided by the upper and lower approximation space of the random set. Then the optimal partition is obtained according to the approximate equivalence relation of the upper and lower approximation. Based on the optimal partition, an improved dark channel theory is presented to enhance each region of the image. After that, sonar images with different backgrounds are used to test the proposed method. The experimental results show that the gray distribution of the sonar image enhanced by this algorithm is more uniform and the boundary details are clearer. The proposed algorithm has the advantage of solving the optimal division for the set of pixels with approximate grayscale. Moreover, the proposed algorithm can get better image enhancement effect in the premise of maintaining the texture of the sonar images.

[1]  Feng Xue,et al.  Remote Sensing Image Enhancement Via Edge-Preserving Multiscale Retinex , 2019, IEEE Photonics Journal.

[2]  Dacheng Tao,et al.  An Underwater Image Enhancement Benchmark Dataset and Beyond , 2019, IEEE Transactions on Image Processing.

[3]  Mounir Kaaniche,et al.  Efficient Enhancement of Stereo Endoscopic Images Based on Joint Wavelet Decomposition and Binocular Combination , 2019, IEEE Transactions on Medical Imaging.

[4]  M. Rahnemoonfar,et al.  Automatic Seagrass Disturbance Pattern Identification on Sonar Images , 2019, IEEE Journal of Oceanic Engineering.

[5]  Lifeng He,et al.  Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand-dust image enhancement , 2020, IET Image Process..

[6]  Jing-Wein Wang,et al.  Color face image enhancement using adaptive singular value decomposition in fourier domain for face recognition , 2016, Pattern Recognit..

[7]  Wei Liu,et al.  Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Guohui Zhang,et al.  A Novel Image Tag Completion Method Based on Convolutional Neural Transformation , 2017, ICANN.

[9]  Rui Ma,et al.  Ultrasound intima-media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet-based orientation-selective filter. , 2016, Medical physics.

[10]  Jin Wang,et al.  Lightweight deep network for traffic sign classification , 2019, Annals of Telecommunications.

[11]  Pamela C. Cosman,et al.  Underwater Image Restoration Based on Image Blurriness and Light Absorption , 2017, IEEE Transactions on Image Processing.

[12]  Min Zhang,et al.  High Precision Implementation With Design Considerations and Experimental Tracking Results for Single-Sensor Optical Communication Terminal , 2019, IEEE Photonics Journal.

[13]  Xi Chen,et al.  Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features , 2019, IEEE Access.

[14]  Yun Shi,et al.  Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images , 2017, Sensors.

[15]  Xi Chen,et al.  Multiscale fast correlation filtering tracking algorithm based on a feature fusion model , 2019, Concurr. Comput. Pract. Exp..

[16]  Ana Claudia Patrocinio,et al.  CLAHE Parameters Effects on the Quantitative and Visual Assessment of Dense Breast Mammograms , 2019, IEEE Latin America Transactions.

[17]  Ahmad Shahrizan Abdul Ghani,et al.  Image contrast enhancement using an integration of recursive-overlapped contrast limited adaptive histogram specification and dual-image wavelet fusion for the high visibility of deep underwater image , 2018 .

[18]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Guy Nason,et al.  Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram , 2015, PloS one.

[20]  Xiaojuan Li,et al.  An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement , 2016, Sensors.

[21]  Wan-Jin Kim,et al.  Efficient edge-preserved sonar image enhancement method based on CVT for object recognition , 2019, IET Image Process..

[22]  T. Sree Sharmila,et al.  A wavelet transform based contrast enhancement method for underwater acoustic images , 2018, Multidimens. Syst. Signal Process..

[23]  Vijanth S. Asirvadam,et al.  Image enhancement based on contourlet transform , 2014, Signal, Image and Video Processing.

[24]  Subramaniam Parasuraman,et al.  Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm , 2016, Appl. Soft Comput..

[25]  Yong Xu,et al.  Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement , 2016, IEEE Access.

[26]  Jian Zhang,et al.  Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring , 2018 .

[27]  Fan Zhao,et al.  Gaussian mixture model-based gradient field reconstruction for infrared image detail enhancement and denoising , 2016 .

[28]  Wei Zhang,et al.  A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors , 2015 .