基于空间自适应加权SVTV的电子商务评论图像优化：消费者信任与平台绩效提升研究

Denoising Optimization of E-Commerce Review Images Based on Spatially Adaptive Weighted SVTV: Enhancing Consumer Trust and Platform Performance

摘要: 在电子商务中，用户生成的评论图像是构建社区信任和影响消费者购买决策的关键因素。然而，这些图像常因拍摄设备和技术限制存在噪声、模糊等问题，影响商品信息传递并加剧信息不对称。为解决这一问题，本研究提出一种基于空间自适应饱和度明度–全变差加权的图像优化模型(WSVTV)。该模型在抑制噪声的同时，智能区分平坦区域与纹理边缘，最大限度保留商品关键细节(如织物纹理、Logo)。在1200张真实电商评论图像上的实验表明，WSVTV在视觉清晰度、可辨识度及主观评价上均显著优于主流方法。更重要的是，优化后的图像能够增强消费者对商品特征的识别和信任，提升购买决策准确性，同时为平台改善UGC展示、优化运营策略提供实践参考。

Abstract: In e-commerce, user-generated review images (UGC) play a critical role in building community trust and influencing consumer purchase decisions. However, these images often suffer from noise and blur due to device limitations and shooting conditions, which hinder effective product information delivery and exacerbate information asymmetry. To address this issue, this study proposes a spatially adaptive weighted saturation-value total variation (WSVTV) model for image optimization. The model intelligently differentiates between flat regions and textured edges, suppressing noise while preserving key product details such as fabric texture and logos. Experiments on a dataset of 1,200 real e-commerce review images demonstrate that WSVTV significantly outperforms mainstream methods in visual clarity, feature recognizability, and subjective evaluations. Importantly, the optimized images enhance consumers’ ability to identify product features and build trust, improving purchase decision accuracy while providing actionable insights for platforms to enhance UGC presentation and optimize operational strategies.