Benefitting from the development of computer vision, computed tomography (CT) images have been used for assisting doctor's clinical diagnosis and improving the diagnostic efficiency. However, there exist some issues in medical images, such as low contrast, obscure detail, and complex noise due to the restriction of the system and the equipment in the process of imaging, medical images. To resolve these issues, a novel lung CT image enhancement method based on total variational framework combined with wavelet transform is proposed. Firstly, low-frequency structure layer with low contrast and high-frequency details layer with complex noise signals are acquired by decomposing the original image using total variational framework. Then, through the analysis of the histogram distribution characteristics of CT image, structure layer requires contrast enhancement; at the same time, the detail layer performs wavelet transform adaptive threshold denoising to remove noise. Finally, weight fusion of processed structure layer and details layer is performed to obtain the final fusion enhancement CT images. Experimental results show that the proposed method can enhance the contrast of clinical lung CT images, improve the clarity of details, and effectively suppress artifacts and complex noises. Contrast and sharpness-objective indicators-prove the proposed method's advantages. Subjectively, the proposed method performs superior over other existing CT enhancement methods, which achieves a better visual recognition.