With the rapid growth of global maritime transportation, accurate ship instance segmentation from SAR images has become a core technology for maritime surveillance and management. However, speckle noise, significant scale variations, and the presence of dense and structurally ambiguous targets in complex nearshore SAR scenes pose substantial challenges to feature extraction, multi-scale representation, and precise boundary delineation in instance segmentation. Therefore, we have proposed YOLO-Argus, an enhanced model for SAR ship instance segmentation. First, a spatial parallel context perception (SPCP) module is introduced to construct a dual-path feature extraction framework using multi-dilated convolutions, thereby improving multi-scale perception, particularly for small targets in complex backgrounds. Second, a multi-scale edge fusion (MSEF) module integrates edge enhancement with contextual semantic information to improve discrimination of blurred boundaries and contour structures. Furthermore, an adaptive tanh unit (ATU) embeds adaptive normalization mechanisms into attention blocks, effectively suppressing speckle noise while preserving fine-grained details. Finally, a multi-scale channel decoupling (MSCD) module leverages grouped and multi-scale convolutions to jointly optimize feature representations, enhancing robustness under noise interference. Experimental results on the public HRSID and PSeg-SSDD datasets quantitatively confirmed the effectiveness of YOLO-Argus. Compared with the baseline model, YOLO-Argus improves $A{P}_{50}$ and $A{P}_{75}$ by 4.3 and 4.7 percentage points on HRSID, and by 3.5 and 9.2 percentage points on PSeg-SSDD, respectively. The proposed method also achieved superior segmentation accuracy compared with existing state-of-the-art methods, including both general-purpose instance segmentation models and SAR ship-specific approaches. These results indicate that the proposed approach effectively mitigates the interference of noise, scale variation, and background complexity in SAR images, providing a more reliable solution for ship instance segmentation.

Image style transfer is a research hotspot in the field of computer vision. For this job, many approaches have been put forth. These techniques do, however, still have some drawbacks, such as high computing complexity and content distortion caused by inadequate stylization. To address these problems, PhotoGAN, a new Generative Adversarial Network (GAN) model is proposed in this paper. A deeper feature extraction network has been designed to capture global information and local details better. Introducing multi-scale attention modules helps the generator focus on important feature areas at different scales, further enhancing the effectiveness of feature extraction. Using a semantic discriminator helps the generator learn quickly and better understand image content, improving the consistency and visual quality of the generated images. Finally, qualitative and quantitative experiments were conducted on a self-built dataset. The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques. It not only performed excellently on objective metrics but also appeared more visually appealing, particularly excelling in handling complex scenes and details.