noisereduction is a fundamental early stage in X-ray image performance evaluation. In this study, the median-modified Wiener filter (MMWF) algorithm based on a nonlinear adaptive spatial filter is compared to two gen...
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noisereduction is a fundamental early stage in X-ray image performance evaluation. In this study, the median-modified Wiener filter (MMWF) algorithm based on a nonlinear adaptive spatial filter is compared to two general noise-reduction techniques using median and Wiener filters to confirm an excellent denoising approach in X-ray images. To acquire images, a high-resolution complementary metal-oxide-semiconductor (CMOS) radio-magnetic X-ray imaging system (exposure conditions: 100, 400, and 700 mu A) and rat phantom were used. The performances of the denoising methods were evaluated in terms of contrast-to-noise ratio (CNR), coefficient of variation (COV), and no-reference image quality assessment using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). The average results for CNR, COV, and BRISQUE in the acquired X-ray image using the MMWF algorithm were 1.08, 1.10, and 1.03 times higher than those of the noisy image, respectively. On average, the MMWF algorithm provided better image restoration than general noise-reduction techniques and was found to be most effective in relatively lower exposure conditions.
Weldment X-ray image processing has great significance on the subsequent segmentation and extraction of weld seam and defects. In the current study, X-ray image for laser welding of aluminum alloy was adopted as exper...
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Weldment X-ray image processing has great significance on the subsequent segmentation and extraction of weld seam and defects. In the current study, X-ray image for laser welding of aluminum alloy was adopted as experimental object. A new image denoising algorithm was proposed, which was combined with weighted adaptive median filter and noise-reduction algorithm based on wavelet transform. An improved adaptive fuzzy enhancement algorithm was put forward, which was based on traditional Pal-King fuzzy enhancement algorithm. Different noise-reduction algorithms were performed to denoise the X-ray image. Comprehensive evaluation of noise-reduction effect was conducted by comparing the processing effect, 3D grayscale distribution, and image quality evaluation index after different denoising algorithms. Moreover, fuzzy entropy and fuzzy index were used to estimate the enhancement effect of traditional Pal-King fuzzy enhancement algorithm and improved adaptive fuzzy enhancement algorithm. The results revealed that the noise-reduction effect and the image quality obtained by proposed algorithm were better than separately using weighted adaptive median filter or noise-reduction algorithm based on wavelet transform. Furthermore, after processing with improved adaptive fuzzy enhancement algorithm, the image detail information was more prominent, and sense of hierarchy was stronger.
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