Medical image denoising using optimal attention block-based pyramid denoising network

Over the past two decades, with advancement of computing technologies and applications has significantly enhanced medical imaging and diagnostic processes. The integration of these advanced computing resources, has improved the diagnosis and treatment of various diseases. However, medical imaging technologies often involve noise that makes the diagnosis process more challenging to distinguish among different areas or objects with medical images. Noise and artifacts can easily infiltrate medical images. This results in denoised images to lose some important information. Extracting useful information from noisy images it is a major challenge. To tackle this issue denoising is a crucial pre-processing step to preserve high-quality recovered images. This paper is dedicated to solving such an issue and proposes a hybrid model to handle it. The paper presented an optimal attention block (OAB) based Pyramid denoising Network (OABPDN) whose function is to estimate the optimal co-efficient for image denoising. The model is composed of an attention block that extracts optimal weight for noise component estimation using cuckoo search optimization (CSO). The entire OABPDN is composed of three processing units i.e., optimal pre-processing block (OPB), multi-scale pyramidal network integrated with OAB, and pyramidal feature selection block (PFSB). The result was performed for different noise scales and with different types of noise such as Gaussian, speckle and Poisson noise. These noises are induced in the dataset artificially. The experiment analysis was performed on different types of medical images. The CHASEDB1, MRI, and Lumbar Spine datasets were used for result evaluation. The result was evaluated with varying OAB Parameters, varying noise levels, and noise type. Then the proposed OABPDN was compared to existing models and it was observed that the proposed OABPDN model outperforms better. The model shows approx. 2–3 % improvement of PSNR and SSIM over existing state-of-art models.