Image Enhancement Network Architecture for Multidimensional Fusion of Medical Imaging Data under Intense Light Interference

Due to the limitations of imaging equipment and environment, the acquired medical images usually have a certain degree of noise and artifacts, which leads to the degradation of the quality of medical images and affects the doctors’ clinical diagnosis of the condition. In this paper, the Gauss-Laplace operator is used to perform normalized filtering on medical images to reduce the influence of noise and improve the convolution effect of images. Through the CLAHE algorithm, the histogram is optimized for equalization, and the network architecture of the image is designed in this way. The quality of the enhanced image is evaluated through experimental design and dataset processing. In the evaluation of subjective and objective metrics, the PSNR and SSIM metrics of the images in SR × 2 are improved by 1.576 dB and 0.997 dB, respectively, on the BraTS dataset. This algorithm’s subjective score is the most high among the four enhancement algorithms, with an average score of 8.25, which aligns with the objective evaluation results. Among the image enhancement results, this paper’s algorithm better adjusts the histogram distribution with h(k) distribution ranging from 0.526-4.215, which is better than other enhancement algorithms in detail enhancement.