In recent years， significant progress has been made in applying Generative Adversarial Network （GAN） in Low-Dose Computed Tomography （LDCT） image denoising. However， the existing methods face challenges such as insufficient modeling capability for complex noise distribution and limited ability to preserve structural details. Therefore， a multi-path GAN for LDCT image denoising — Trident GAN was proposed. Firstly， a feature guided generator Trident Uformer was designed. In this generator， a Feature Polymerization Attention （FPA） module was added at the bottleneck layer of the U-Net structure， thereby solving the problem of low spatial resolution in a U-shaped structure. Secondly， a multi-path feature extraction submodule Trident Block was designed， and in each of the three blocks， a Local Detail Enhancement Block （LDEB） was introduced to extract detailed features， a Lightweight Channel Attention Block （LCAB） was incorporated to enhance channel features， and a Spatial Interaction Attention Block （SIAB） was utilized to capture important spatial features， respectively. Within the SIAB， a multi-level interactive attention function and evaluation mechanism were employed to design a Spatial Context Attention Mechanism （SCAM）， which addresses the limitations of single-attention mechanisms. Finally， a Multi-Feature Fusion （MFF） module was designed to realize feature aggregation at the end of the three blocks， and model both local detail information and global semantic information， and solve the problem of discontinuous details across different levels. Furthermore， the Multi-Scale Pyramid Discriminator （MSPD） was used to check the quality of the generated results at different dimensions， and guide the generation of globally consistent images. Experimental results show that Trident GAN achieves the average Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity Index Measure （SSIM） of 31.519 3 dB/0.883 0 and 33.633 1 dB/0.947 8， respectively， on Mayo and Piglet datasets. Compared with High-Frequency Sensitive GAN （HFSGAN）， this method has the number of parameters reduced by 75.58% and the test time reduced by 36.36%. It can be seen that compared with the existing methods such as HFSGAN， Trident GAN improves image quality with less computational load.

This paper introduced the research works on all kinds of chain code used in image processing and pattern recognition and a new chain code named Improved Compressed Vertex Chain Code (ICVCC) was proposed based on Compressed Vertex Chain Code (CVCC). ICVCC added one code value compared with CVCC and adopted Huffman coding to encode each code value to achieve a set of chain code with unequal length. The expression ability per code, average length and efficiency as well as compression ratio with respect to 8-Directions Freeman Chain Code (8DFCC) were calculated respectively through the statistis a large number of images. The experimental results show that the efficiency of ICVCC proposed this paper is the highest and compression ratio is ideal.