Image Denoising Based on an Improved Wavelet Threshold and Total Variation Model

With advancements in computer vision, computed tomography (CT) has been employed to aid clinicians in clinical diagnosis, thereby enhancing di-agnostic efficiency. However, during the medical imaging process, medical images often suffer from issues such as blurring and complex noise as a result of system and equipment limitations. To address these challenges, we propose a novel image enhancement method integrating improved wavelet thresholding with total variation model denoising. Initially, the image is de-composed into high- and low-frequency sub-bands using wavelet decomposition. Subsequently, improved wavelet thresholding is employed to denoise the high-frequency sub-bands, which contain detail and texture information, whereas the total variation model is applied to denoise the low-frequency sub-bands containing the overall structure and rough outline information of an image. Finally, reconstruction is performed using an inverse wavelet transformation. Experimental results demonstrate that the proposed algorithm not only effectively suppresses complex noise in images and enhances the contrast of clinical pulmonary CT images but also preserves the natural appearance of images and enhances texture details and edge features. The proposed method exhibits superior performance compared with existing CT enhancement methods, achieving enhanced visual perception.