This paper presents a hybrid medical image fusion (MIF) technique (SWT-PnP-DnCNN) that combines multiscale decomposition, spatial-frequency-driven fusion, and deep denoising priors to efficiently integrate MIF images. The SWT-PnP-DnCNN begins with the Stationary Wavelet Transform (SWT) to decompose input medical images into low-frequency (LFSBs) and high-frequency (HFSBs) subbands. The LFSBs are fused using spatial frequency-based weighted averaging, effectively integrating overall intensity and contrast information. For the HFSBs, a local energy and max-selection strategy is adopted to retain salient edge features from the source images. Following the initial fusion, a Plug-and-Play (PnP) optimization strategy is applied to improve this fused image. This step uses a pretrained DnCNN model as a deep denoiser, serving as an implicit image prior in a model-driven iterative framework. Each iteration alternates between a data consistency step and a denoising step, significantly reducing artifacts and enhancing structural fidelity in the result. The effectiveness of SWT-PnP-DnCNN is demonstrated on benchmark CT-MRI, MRI-PET, and MRI-PET datasets. Extensive evaluation against classical hybrid strategies and recent CNN-based fusion methods shows that SWT-PnP-DnCNN achieves the best performance across standard metrics. We further include mean±std reporting and paired t-tests, confirming statistically significant improvements (p < 0.05). Ablation studies validate each design choice by comparing SWT-only vs. SWT+PnP and evaluating denoiser alternatives, with sensitivity to PnP iterations, regularization strength, and SWT levels. The runtime analysis clarifies feasible deployment, particularly in offline or cloud-based environments. Overall, SWT-PnP-DnCNN emerges as a robust, interpretable, and clinically valuable solution for enhancing MIF in medical imaging applications.

The main aim of image denoising is to improve the visual quality in terms of edges and textures of images. In Computed Tomography (CT), images are generated with a combination of hardware, software and radiation dose. Generally, CT images are noisy due to hardware/software fault or mathematical computation error or low radiation dose. The analysis and extraction of medical relevant information from noisy CT images are challenging tasks for diagnosing problems. This paper presents a novel edge preserving image denoising technique based on wavelet transform.
The proposed scheme is divided into two phases. In first phase, input CT image is separately denoised using different patch size where denoising is performed based on thresholding and its method noise thresholding. The outcome of first phase provides more than one denoised images. In second phase, block wise variation based aggregation is performed in wavelet domain.
The final outcomes of proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of proposed method is superior to existing methods in terms of visual quality, PSNR and Image Quality Index (IQI).