Underwater Image Saliency Detection Based on Improved Histogram Equalization

In order to solve the problem of unsatisfactory detection effect of underwater visual saliency map, an image saliency detection algorithm based on improved histogram equalization is proposed. Underwater images are often not clear enough because the refraction of light underwater causes insufficient image resolution. Therefore, in order to solve the existing problems of traditional histogram equalization algorithm, an improved histogram equalization method is proposed to enhance the quality of images, which makes the saliency regions smoother and clearer. In this paper, the simulation experiments were conducted on UIEBD dataset and DLOU_underwater dataset. The experimental results show the effectiveness, robustness and accuracy of the proposed algorithm.

[1]  Lina J. Karam,et al.  A MATLAB-based framework for image and video quality evaluation , 2010, 2010 Second International Workshop on Quality of Multimedia Experience (QoMEX).

[2]  WU Cheng-mao,et al.  Studies on Mathematical Model of Histogram Equalization , 2013 .

[3]  王烨蕾,et al.  基于分层PCA技术的显著性目标检测算法Saliency Detection Based on Hierarchical PCA Technology , 2018 .

[4]  Chang-Hsing Lee,et al.  An adult image identification system employing image retrieval technique , 2007, Pattern Recognit. Lett..

[5]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

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

[9]  Lihi Zelnik-Manor,et al.  What Makes a Patch Distinct? , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Li Jin.  Development and Prospect of Image Contrast Enhancement , 2013 .

[11]  Sabine Süsstrunk,et al.  Salient Region Detection and Segmentation , 2008, ICVS.

[12]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[13]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[15]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[16]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.