Automated enhancement of post-infectious lung CT images using multi-technique noise detection and denoising with...

Infectious lung diseases pose a serious public health risk; can cause long-term conditions like fibrosis, loss of lung function and chronic lung diseases like COVID-19, pneumonia and tuberculosis. Persistent lung abnormalities can result from these complications and need to be accurately detected and analysed. The deep ensemble learning technique was utilized to extract high-quality images from raw computed tomography (CT) scan images by incorporating noise detection and reduction techniques. In this study, BM3D denoising technique has been used for noise reduction in the post-infectious CT scan image to improve its quality by using the automated detection of noise. The main objective of this research is to improve the image quality in the pre-processing stage using various noise detection methods such as Blur Laplacian, Noise Entropy, Noise Power Spectrum Density and Wavelet Transform. Each noise detection technique produces the amount of noise detected in terms of numeric variance. Based on the highest numeric variance, the noise type is detected automatically. Then, the BM3D denoising technique was applied to yield an effective image for further processing. The proposed system validates noise reduction in terms of PSNR, SSIM, and edge preservation index. Thus, the results provide a significant enhancement in image clarity and structural clarity to support downstream diagnostic tasks, supporting its potential utility as a pre-processing step for clinical and research applications.