Given the fuzziness and unevenness of infant brain images

infant brain MR image enhancement has become an important topic. Traditional fractional differential algorithm is used essentially to expand the difference between adjacent gray pixels. The order of traditional fractional differential fluctuates strongly in the area of intense gray change

thereby leading to image over-enhancement and introduction of noise. This condition is likely to cause the infant brain MR image enhancement effect to be limited and over-enhanced. To solve the aforementioned problems

we propose an adaptive fractional differential MR image enhancement algorithm based on non-local means value. Otsu algorithm and texture roughness are used to determine the initial number of fractional order. The pixel gray value of the Otsu algorithm is replaced with the average gradient matrix

and the image is divided into two parts by threshold () determined by Otsu algorithm and gradient matrix

texture section

and edge section. Roughness is the physical quantity that describes the size and distribution of the particle. The larger the size of the base elements and the farther the distance between them

the rougher is the texture. In the smooth region of the image

roughness is smaller

and roughness is relatively larger in rich texture regions. To suppress noise interference

a larger range of texture information is integrated into the non-local means

which is used to determine the final number of fractional order. The order of the current search block is determined by initial order matrix. The non-local means is used to filter the order matrix of the searching area. The weight contribution of the neighborhood block to the central block is determined by the similarity of their order matrix. If their structures are similar

the weight is large. Otherwise

the weight is small. This condition can effectively reduce the number of mutations caused by noise

edge

and other factors

thereby effectively retaining the image details after filtering. Finally

the fractional-order filter original image is used and the final enhanced image is obtained. In this paper

the information entropy

average gradient

and spatial frequency are obtained as statistical indexes. Experimental results prove that this algorithm has superior performance in image enhancement. Information entropy is higher by 0.2% to 12% than that of compared algorithms. Average gradient is higher by 5% to 59% than that of compared algorithms. Spatial frequency is higher by 6% to 59% than that of compared algorithms. Only the information entropy is slightly lower than the fractional-order differential and wavelet decomposition of the image enhancement algorithm

and the average gradient and spatial frequency is slightly lower than that of the adaptive fractional-order differential algorithm. The algorithm we propose is more capable of enhancing texture details and is effective in suppressing new noise than the compared algorithms in this paper. The algorithm is also applicable to general fuzzy images and has good application potential.