In recent years, fuzzy image enhancement methods have been widely applied in image enhancement, which generally consists of three steps: fuzzification, modify membership(using intensifier (INT) operator), and defuzzification. This paper proposed a new INT operator used in fuzzy image enhancement. The INT operator is adjustable for different test images. The image enhancement method is as follows, firstly, calculate the image threshold (T ) using the OTSU method. Secondly, calculate pivotal point p corresponding to T , and find the corresponding INT operator function. Finally, use the INT operator in fuzzy Image Enhancement. The INT operator is used multiple times in the image processing process to obtain multiple result images. Comparative experiments show that the proposed new INT operator has better image enhancement effect when INT operator is applied at the same number of times. On the other hand, more intermediate process result images can also be obtained through the proposed new INT operator. More result images can provide material resources for the subsequent image processing.
Fuzzy image enhancement is an important method in the process of image processing. Fuzzy image enhancement includes steps: gray-level fuzzification, modifying membership using intensifier (INT) operator, and obtaining new gray-levels by defuzzification. This paper proposed an adjustable INT operator with parameter k. Firstly, the image’s pixels are divided into two regions by the OTSU method (low and high region), and calculate the pixels’ membership by fuzzification in each region. Then, the INT operator reduce pixels’ membership in the low region and enlarge pixels’ membership in the high region. The parameter k is determined base on the pixel’s location information (neighborhood information), and plays an adjusting role when the INT operator is working. And finally, the result image is obtained by the defuzzification process. In the experiment results, the fuzzy image enhancement with the adjustable intensifier operator achieves a better performance.
Image enhancement is a significant field in image processing. This paper proposes an enhancement method based on an S-sharp function of grayscale transformation and neighborhood information. Firstly, a function is established based on the sine function. Then, the image threshold is added into the function. Finally, the result grayscales are modified by parameter, where parameter is determined by the image pixel neighborhood information. In general, in the result image, each pixel grayscale is determined by both the sine function with threshold and the parameter . In the experiment results, the NIEM method (we proposed) achieves better performance than the comparison algorithms. It gets the smallest MSE and the highest PSNR, SSIM. In image Lena test, MSE value:330.8151, PSNR value:22.9350, and SSIM value: 0.9451. In image Pout test, MSE value:132.0988, PSNR value:26.9218, and SSIM value: 0.9604.
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