Abstract:
Because of the suspended solids in the underwater environment, and light absorption and scattering, underwater sea cucumber image has the weaknesses of illumination uneven, low contrast, various kinds of noise and soon, causing difficulties for the identification for underwater robots.In this paper, image enhancement technology for underwater sea cucumber image was studied, and a method called Contrast limited Adaptive Histogram Equalization(CLAHE) was proposed to deal with the underwater sea cucumber images.We used sea cucumbers which grew up in Shandong Haiyang Thousand Island Lake aquaculture base as the experimental subject, and recorded video by artificial dive underwater with the digital camera(Canon Power Shot G12) in July 2015, in order to get all kinds of images(single sea cucumber, sea cucumber with substrates, many sea cucumbers) filtrated the pictures from all the videos, at last we got about 200 images which contained a variety of circumstances.The image enhancement algorithm used in the article has the following steps: Firstly, the original image was divided into several sub regions of the same size(each sub region was continuous and non overlapping).Secondly, we selected a specific value to make sure that the number of pixels in each gray level was no more than this value, and then used the specific value to intercept the histogram of each sub region, and the intercepted pixels were evenly distributed to each gray level.Thirdly, we made histogram equalization to the gray histogram of each sub area after shearing.Fourthly, bilinear interpolation was used to get gray value of the central point of each sub block, and taking these points as reference points, the mapping of each pixel point in the image was determined by the mapping of by the four reference points around.Finally, the enhancement of the underwater sea cucumber images was finished by using the method of Contrast limited Adaptive Histogram Equalization, and we also used other image processing methods (such as: histogram equalization, linear conversion) dealing with sea cucumber images.Then through subjective judgment(observe the change of processed image and its histogram, compare the changes and find out which method is the best one), we found out that images processed by HE had the shortcoming of noise over enhancement, processed by Linear conversion turn up color distortion.Also by evaluation functions: Mean squared error(MSE), Peak signal to noise rate(PSNR), and information entropy were used for objectively evaluated the method used in this paper and the other methods.We got the average value of MSE and PSNR, the information entropy and processing time by processing 200 images, and it turned out that the value of the method used in this paper was better than the other methods: the average of MSE was about 29.570 5, PSNR was about 24.119 4, and information entropy was about 6.936 4.Generally speaking, the value of MSE was smaller, the result was better, and the value of PSNR and information entropy was bigger, the result was better.The study showed that CLAHE took great advantages of several other methods to achieve good results.To get better parameters which are suitable for underwater sea cucumber image, we also improved the algorithm by studying the related parameters, while the split window was too large, the strengthen will be weaken; too small window will cause over enhancement, and found that the effect was best when the window size was about 32×32.The experimental results show that: CLAHE algorithm shows better performance in improving the quality of underwater sea cucumber image and maintaining the details of the images than the other methods, the value of objective evaluation function has a better promotion, and all of this provides convenience for the identification of underwater robot positioning.