# X-ray Image Enhancement Based on Adaptive Gradient Domain Guided Image Filtering
###### Abstract  
Due to the contrast of X-ray images being low, significant elements including organs, bones, and nodules are very difficult to identify, so contrast enhancement is necessary. In this paper, an X-ray image enhancement algorithm based on adaptive gradient domain guided image filtering is proposed. The amplification factor in the gradient domain guided image filtering needs to be set manually; it needs to constantly adjust the parameters to achieve the best enhancement effect, and this also increases the computational complexity. In order to solve this problem, an adaptive amplification factor is defined in this paper, and  
terms of contrast enhancement. [PERSON] et al. [19] proposed an image enhancement technique based on contourlet transform. This algorithm achieves enhancement by adjusting the decomposed contourlet coefficients, but as the contourlet transform does not have shift-invariance, this method can lead to the appearance of the pseudo-Gibbs phenomenon. The NSCT is an improved version of contourlet transform, which has shift-invariance, and the pseudo-Gibbs phenomenon can be effectively avoided in the application of image processing. [PERSON] et al. [20] introduced an enhancement technique using NSCT; the gamma correction and adaptive threshold are used to process the low-frequency and high-frequency components, respectively. The unsharp filter is used to process the reconstructed image achieved by inverse NSCT. The result is a dramatic advance in image contrast. The enhancement methods based on NSCT show good performance, but NSCT has a high computational complexity. Compared with the NSCT, shearlet has lower computational complexity and better sparse representation characteristics. [PERSON] et al. [21] proposed a medical image enhancement algorithm using singular value decomposition in NSST domain, and a good image enhancement performance was achieved. [PERSON] et al. [22] introduced another enhancement method utilizing nonsubsampled shearlet transform for X-ray images.  
In recent years, guided image filtering is very popular in image processing fields [23], such as image enhancement, image fusion, image classification, image restoration, image super-resolution, image dehazing, etc. Based on guided image filtering, some extended versions were proposed, such as weighted guided image filtering (WGIF) [24], gradient domain guided image filtering (GDGIF) [25], weighted guided image filtering with steering kernel (SKWGIF) [26], anisotropic guided filtering (AnisGF) [27], effective guided image filtering [28], side window guided filtering (SWGF) [29], weighted aggregation for guided image filtering (WAGIF) [30], and robust double-weighted guided image filtering [31]. Among them, the effect of gradient domain guided image filtering in image enhancement has a good performance status. But the amplification factor in the GDGIF needs to be set manually; it needs to constantly adjust parameters to obtain a better enhanced image. This also increases the computational complexity. In order to deal with this weakness, an adaptive amplification factor is defined in this paper, and the proposed algorithm is applied to X-ray image enhancement.