【摘要】 目的 线稿提取是指利用边缘检测技术从原始图像中提取出具有语义连续性的轮廓和边缘信息，为动漫上色、风格迁移等下游任务提供结构化输入。针对现有线稿提取模型在复杂纹理场景下，提取结果仍存在线条不纯净、背景伪影等问题，提出基于跨层级响应融合与联合损失优化的高保真线稿提取模型CLEAR-Net(cross-level edge aggregation response network)。方法 引入反卷积改进U~2-Net提取图像不同层级的响应；提出动态侧边聚合模块实现跨层级响应优化整合；针对复杂纹理场景下所产生的背景伪影问题，提出了一种新的监督机制——背景抑制损失，对背景伪影进行像素级动态惩罚；设计联合损失函数，联合背景抑制损失与改进的交叉熵损失，在抑制背景伪影的同时优化生成线条的质量。为构建可靠的评估基准，联合专业艺术团队构建首个高精度手绘线稿数据集ArtLine-2K，包含2 000组渲染图—线稿对，并经数据增强扩充到10 000对，解决了当前线稿提取任务中高质量标注数据缺乏的问题。实验在ArtLine-2K数据集上与先进方法进行了比较。结果 实验结果表明，CLEAR-Net生成结果与真实标注的差异肉眼难以区分，其核心精度指标：均方误差(mean squared error,MSE)和平均绝对误差(mean absolute error,MAE)分别为0.000 247和0.004 810，与真实标注的误差达到亚像素精度(MAE<0.005)，在ArtLine-2K上取得了突破性能。生成结果经专业画师评估，可以直接进行二次创作，同时也在ArtLine-2K上进行了消融实验以验证提出方法的有效性。结论 CLEAR-Net模型不仅优化整合了原始图像各层级的响应，而且提出了新的监督机制，解决了线稿提取任务中线条不纯净、背景伪影等问题。更多还原

【Abstract】 Objective Line drawing extraction is one of the key tasks in the fields of computer vision and image processing.It aims to extract contour and edge information automatically with semantic continuity and structural consistency from the original image by using edge detection and feature learning techniques.In this way,a high-quality structured input for downstream tasks,such as animation coloring,style transfer,image generation,and illustration restoration,is provided.This task not only requires the model to identify the main outline of the object accurately but also needs to maintain the continuity of the lines and the rationality of the overall structure while suppressing the interference of irrelevant background and texture details.When facing complex textures and rich background images,the existing line drawing extraction methods can obtain relatively clear lines in regular scenes;however,balancing the detail fidelity of the lines and the purity of the background is difficult,and problems such as line breakage,blurred contours,loss of local details,and background artifacts are prone to occur.These problems cause the extraction results to lack semantic integrity and artistic consistency,thereby reducing the input quality of downstream tasks and making the demands of actual creation and industrial applications for high-precision line drawings difficult to meet.In response to the above problems,this study proposes a highfidelity line drawing extraction model,namely,cross-level enhanced aggregation and refinement network(CLEAR-Net),based on cross-level response fusion and joint loss optimization.This model fully utilizes multiscale semantic information by integrating feature responses at different levels and introduces a joint optimization strategy,thereby effectively improving the quality of line extraction.It suppresses background artifacts while ensuring structural consistency,thereby obtaining pure line drawing results.Method This study made structural improvements based on U2-Net and introduced a deconvolution module to enhance the model’s response ability to features at different levels.The model can fully restore the spatial detail information of the deep response by adding deconvolution operations in the upsampling stage;thus,delicate edge structure extraction is achieved in the multiscale feature fusion process.Subsequently,a dynamic side aggregation module was proposed to achieve dynamic fusion and optimization of cross-level features.This module can automatically allocate aggregation weights based on the correlation between features of different layers,strike a balance between global structural information and local texture details,and effectively enhance the coherence and integrity of the line structure.A background suppression supervision mechanism is proposed for the common background artifact problem in complex texture scenes.This mechanism enables the model to penalize the pseudo-responses in the background area dynamically.It also effectively reduces the interference of background noise.As a result,the purity and robustness of the results are enhanced.A joint loss function combining the background suppression loss with the improved cross-entropy loss is designed to enhance the quality of the generated results further.As a result,the background artifacts are suppressed,and the foreground lines are optimized,thereby achieving a dual improvement in line quality and background purity.Finally,this study was conducted in collaboration with a professional art team to build the first high-precision hand-drawn dataset,ArtLine-2K,which contains 2 000 pairs of high-quality rendered line drawing samples covering various painting styles and complex scenes.This dataset was expanded to 10 000 pairs of samples through data augmentation.Thus,the problem of scarce high-quality labeled data in the line drawing extraction task is effectively alleviated.A systematic comparison was conducted with multiple advanced methods on the Art Line-2K dataset.Result Experimental results show that the differences between the generated results of CLEAR-Net and the real annotations are difficult to distinguish with the naked eye.The errors of its core accuracy indicators,MSE(0.000 247) and MAE(0.004 810),and the real annotations reach subpixel accuracy(MAE < 0.005),thereby achieving the breakthrough performance on Art Line-2K.The generated results were evaluated by professional painters and could be directly used for secondary creation.The ablation experiments were also conducted on Art Line-2K to verify the effectiveness of the proposed method.Conclusion Experimental results show that CLEAR-Net achieved a breakthrough performance on Art Line-2K.The generated results are almost indistinguishable from the real annotations.The precision index MSE is 0.000 247,and MAE is 0.004 810.Moreover,the error of the proposed model reaches the subpixel level(MAE < 0.005),which is significantly better than that of the existing methods.Compared with other models,Clear-Net performs outstandingly in detail restoration,line continuity,and background purity.The generated line drawings have clear and natural lines with smooth edges and no artifacts.They can be directly used for secondary creation after being evaluated by professional artists.A systematic ablation experiment was carried out on Art Line-2K to verify the effectiveness of the model structure design and loss function.Results show that the introduction of the feature extraction module,side fusion mechanism,background suppression loss,and smooth heating cross-entropy can synergistically and significantly reduce the error.Compared with the benchmark model,the proposed model achieves more than 95% improvement in overall performance.Furthermore,CLEAR-Net still maintains stable performance on lowquality datasets,such as Anime Sketch Colorization Pair,thereby demonstrating excellent cross-domain generalization ability and robustness.更多还原