LapLoss通过拉普拉斯金字塔多尺度损失，提升图像翻译的对比度与细节保留。
LapLoss: Laplacian Pyramid-based Multiscale loss for Image Translation
- 基于拉普拉斯金字塔设计多尺度损失函数，逐层优化图像特征。
- 在SICE数据集上跨光照条件表现最优，显著提升细节保真度。
- 适合需要高保真图像生成的场景，如低光照增强、医学影像处理。
对比度增强是图像到图像翻译（I2IT）中的关键环节，能通过调整像素间明暗差异提升视觉质量。然而，现有方法常难以保留细粒度细节，导致低层特征丢失。本文提出LapLoss，一种基于拉普拉斯金字塔的新型多尺度损失方法，核心在于构建以拉普拉斯金字塔为中心的网络结构。该方法采用多鉴别器架构，每个鉴别器在不同分辨率下运作，以捕捉高层语义特征，同时在混合光照条件下保持低层细节与纹理。通过在金字塔各层级计算损失，平衡重建精度与感知质量，显著提升整体图像生成效果。该框架在SICE数据集上持续优于现有对比度增强技术，展现出卓越的跨光照适应能力。
原文摘要 · Abstract (English)
Contrast enhancement, a key aspect of image-to-image translation (I2IT), improves visual quality by adjusting intensity differences between pixels. However, many existing methods struggle to preserve fine-grained details, often leading to the loss of low-level features. This paper introduces LapLoss, a novel approach designed for I2IT contrast enhancement, based on the Laplacian pyramid-centric networks, forming the core of our proposed methodology. The proposed approach employs a multiple discriminator architecture, each operating at a different resolution to capture high-level features, in addition to maintaining low-level details and textures under mixed lighting conditions. The proposed methodology computes the loss at multiple scales, balancing reconstruction accuracy and perceptual quality to enhance overall image generation. The distinct blend of the loss calculation at each level of the pyramid, combined with the architecture of the Laplacian pyramid enables LapLoss to exceed contemporary contrast enhancement techniques. This framework achieves state-of-the-art results, consistently performing well across different lighting conditions in the SICE dataset.
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