Brightness Fusion and Multi-Scale Optimized Enhancement Algorithm for Fuel Rod DR Images

[Background] A fuel rod is a fundamental unit of a fuel assembly, and it directly impacts the safe operation of nuclear reactors. [Purpose] To efficiently detect internal defects in fuel rods, a high-resolution visual nondestructive testing method, X-ray imaging, is employed. To address the issue of low contrast in fuel rod X-ray DR images, a brightness fusion and multiscale optimized enhancement algorithm for fuel rod DR images is proposed. [Methods] First, the image brightness is corrected using logarithmic and gamma transformations and further refined by incorporating local information fusion. Subsequently, a wavelet function is applied for multiscale decomposition, enhancing and sharpening low-frequency components with Retinex, and filtering high-frequency components using NL_Means. Finally, image enhancement is realized via wavelet reconstruction. [Results] To evaluate the performance of the algorithm, experiments are conducted using the DR images of fuel rods as test subjects. Four representative image quality assessment metrics are employed for quantitative analysis. [Conclusions] The experimental results demonstrate that image brightness fusion and multiscale