Aiming at the problems such as lack of contextual information connection， inaccurate and low-precision segmentation of cervical cell nucleus images， a cervical cell nucleus segmentation network named DGU-Net （Dense-Guided-UNet） was proposed on the basis of improved U-net combined with dense block and U-shaped convolutional multi-scale guided filtering module， which could segment cervical cell nucleus images more completely and accurately. Firstly， the U-net model with encoder and decoder structures was used as backbone of the network to extract image features. Secondly， the dense block module was introduced to connect the features between different layers， so as to realize transmission of contextual information， thereby enhancing feature extraction ability of the model. Meanwhile， the multi-scale guided filtering module was introduced after each downsampling and before each upsampling to introduce obvious edge detail information in the grayscale guided image for enhancement of the image details and edge information. Finally， a side output layer was added to each decoder path， so as to fuse and average all the output feature information， thereby fusing the feature information of different scales and levels to increase accuracy and completeness of the results. Experiments were conducted on Herlev dataset and the proposed network was compared with three deep learning models： U-net， Progressive Growing of U-net+ （PGU-net+）， and Lightweight Feature Attention Network （LFANet）. Results show that compared with PGU-net+， DGU-Net increases the accuracy by 70.06%； compared with LFANet， DGU-Net increases the Intersection-over-Union （IoU） by 6.75%. It can be seen that DGU-Net is more accurate in processing edge detail information， and outperforms the comparison models in segmentation indicators generally.

Aiming at the problems that the Total Variation （TV） minimization method easily leads to image over-smoothing and block effects in Low-Dose Computed Tomography （LDCT） image reconstruction， an LDCT image reconstruction method based on low-rank and TV joint regularization was proposed to improve the visual quality of LDCT reconstructed images. Firstly， a low-rank and TV joint regularization based image reconstruction model was established， thus， more accurate and natural reconstruction results were obtained theoretically. Secondly， a low-rank prior with non-local self-similarity property was introduced to overcome the limitations of only using the TV minimization method. Finally， the Chambolle-Pock （CP） algorithm was used to optimize and solve the model， which improved the solution efficiency of the model and ensured the effective solution of the model. The effectiveness of the proposed method was verified under three different LDCT scanning conditions. Experimental results on Mayo dataset show that compared with the PWLS-LDMM （Penalized Weighted Least-Squares based on Low-Dimensional Manifold） method， NOWNUNM （NOnlocal Weighted NUclear Norm Minimization） method and CP method， at 25% dose， the proposed method increases the Visual Information Fidelity （VIF） by 28.39%， 8.30% and 2.93%， respectively； at 15% dose， the proposed method increases the VIF by 29.96%， 13.83% and 4.53%， respectively； at 10% dose， the proposed method increases the VIF by 30.22%， 17.10% and 7.66%， respectively. It can be seen that the proposed method can retain more detailed texture information while removing noise and stripe artifacts， which verifies that the proposed method has better noise artifact suppression capability.

In recent years， Generative Adversarial Network （GAN） used for Low-Dose Computed Tomography （LDCT） image denoising has shown significant performance advantages， becoming a hot topic in the field. However， the insufficient perception ability of GAN generator for the noise and artifact distribution in LDCT images leads to limit the denoising performance. To address this issue， an LDCT denoising model based on a Dual Encoder-Decoder GAN （DualED-GAN） was proposed. Firstly， a pair of encoder-decoder was proposed to form an artifact pixel-level feature extraction channel for estimating the artifact noise in LDCT images. Secondly， another pair of encoder-decoder was proposed to form an artifact mask information extraction channel for estimating the intensity and location information of artifacts. Finally， the artifact image quality label maps were used to assist in estimating the mask information of artifacts， so that supplementary features were provided for the artifact pixel-level feature extraction channel， thereby enhancing the sensitivity of the GAN denoising network to the distribution intensity of artifact noise. Experimental results show that compared with the sub-optimal model DESD-GAN（Dual-Encoder-Single-Decoder based Generative Adversarial Network）， the proposed model increases the average Peak Signal-to-Noise Ratio （PSNR） by 0.338 7 dB， and the average Structural Similarity Index Measure （SSIM） by 0.002 8 on mayo test set. It can be seen that the proposed model performs better in all terms of artifact suppression， structural preservation， and model robustness.

For the shortcomings of poor interpretation ability and instability in neural network training， a Chambolle- Pock （CP） algorithm optimized denoising network based on Total Variational （TV） regularization， CPTV-Net， was proposed to solve the denoising problem of Low-Dose Computed Tomography （LDCT） images. Firstly， the TV constraint term was introduced into the L1 regularization term model to preserve the structural information of the image. Secondly， the CP algorithm was used to solve the denoising model and obtain specific iterative steps to ensure the convergence of the algorithm. Finally， the shallow CNN （Convolutional Neural Network） was used to learn the iterative formula of the primal dual variables of the linear operation. The neural network was used to calculate the solution of the model， and the network parameters were collected to optimize the combined data. The experimental results on simulated and real LDCT datasets show that compared with five advanced denoising methods such as REDCNN （Residual Encoder-Decoder Convolutional Neural Network） and TED-Net （Transformer Encoder-decoder Dilation Network）， CPTV-Net has the best Peak Signal-to-Noise Ratio （PSNR）， Structural SIMilarity （SSIM）， and Visual Information Fidelity （VIF） evaluation values， and can generate LDCT images with significant denoising effect and the most details preserved.

Concerning the problems of artifacts and loss of image details in the analytically reconstructed image by time-domain filters， a new time-frequency domain Computed Tomography （CT） reconstruction algorithm based on Convolutional Neural Network （CNN） was proposed. Firstly， a filter network based on a convolutional neural network was constructed in the frequency domain to achieve the frequency-domain filtering of the projection data. Secondly， the back-projection operator was used to perform domain conversion on the frequency-domain filtered result to obtain a reconstructed image. A network was constructed in the image domain to process the image from the back-projection layer. Finally， a multi-scale structural similarity loss function was introduced on the basis of the minimum mean square error loss function to form a composite loss function， which reduced the blur effect of the neural network on the result image and preserved the details of the reconstructed image. The image domain network and the projection domain filter network worked together to finally get the reconstructed result. The effectiveness of the proposed algorithm was verified on the clinical dataset. Compared with the Filtered Back Projection （FBP） algorithm， the Total Variation （TV） algorithm and the image domain Residual Encoder-Decoder CNN （RED-CNN） algorithm， when the number of projections is respectively 180 and 90， the proposed algorithm achieved the reconstructed result image with highest Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity （SSIM）， and the least Normalized Mean Square Error （NMSE）.When the number of projections is 360，the proposed algorithm is second only to TV algorithm. The experimental results show that the proposed algorithm can improve the reconstructed image quality of CT image， and it is feasible and effective.