RESUMO
BACKGROUND: Recently, X-rays have been widely used to detect complex structural workpieces. Due to the uneven thickness of the workpiece and the high dynamic range of the X-ray image itself, the detailed internal structure of the workpiece cannot be clearly displayed. OBJECTIVE: To solve this problem, we propose an image enhancement algorithm based on a multi-scale local edge-preserving filter. METHODS: Firstly, the global brightness of the image is enhanced through logarithmic transformation. Then, to enhance the local contrast, we propose utilizing the gradient decay function based on fuzzy entropy to process the gradient and then incorporate the gradient into the energy function of the local edge-preserving filter (LEP) as a constraint term. Finally, multiple base layers and detail layers are obtained through filtering multi-scale decomposition. All detail layers are enhanced and fused using S-curve mapping to improve contrast further. RESULTS: This method is competitive in both quantitative indices and visual perception quality. CONCLUSIONS: The experimental results demonstrate that the proposed method significantly enhances various complex workpieces and is highly efficient.
RESUMO
X-ray images frequently have low contrast and lost edge features because of the complexity of objects, attenuation of reflected light, and scattering superposition of rays. Image features are frequently lost in traditional enhancement methods. In this paper, we use a ray scattering model to estimate coarsely clear images and an encoder-decoder network and multi-scale feature extraction module to add multi-scale and detail information to the images. To selectively emphasize useful features, a dual attention module and UnsharpMasking with learnable correction factors are used. The results of the experiments demonstrate that the method may significantly enhance the quality of x-ray images.
