用三张曝光照片生成高动态范围图像和视觉平衡的普通图像。
Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping
- 通过双注意力融合提取过曝欠曝图像的互补信息，稳定重建。
- 合成的普通图像指导增益图预测，实现高质量动态范围扩展。
- 适合需要同时获取HDR与SDR输出的摄影与视频处理场景。
我们提出DOME-HDR，一种双输出多曝光高动态范围（HDR）重建框架，通过增益图逆色调映射联合生成感知平衡的标准动态范围（SDR）图像和一致的HDR图像。给定三张分档亮度的低动态范围（LDR）输入，DOME-HDR首先使用LoRA适配的潜在扩散模型合成基础SDR图像。一个双交叉注意力融合模块从过曝和欠曝图像中注入互补的结构与色彩线索，同时以中间曝光图像为锚点保持稳定性。合成的SDR图像进一步引导我们的HDR先验引导增益图网络（HPGM），预测空间可变的增益图，实现可靠的动态范围扩展。在Kalantari、Tel和Challenge123数据集上，采用全参考与无参考指标评估，DOME-HDR达到当前最优的HDR重建质量；消融实验进一步验证了双交叉注意力和SDR引导增益图估计的有效性。
原文摘要 · Abstract (English)
We propose DOME-HDR, a dual-output multi-exposure HDR reconstruction framework that jointly produces a perceptually balanced SDR image and a consistent HDR image via gain map inverse tone mapping. Given three bracketed LDR inputs, DOME-HDR first synthesizes a base SDR using a LoRA-adapted latent diffusion model. A dual cross-attention fusion module injects complementary structural and color cues from the under- and over-exposed images while anchoring on the mid exposure for stability. The synthesized SDR then guides HPGM, our HDR Prior-guided Gain Map network, to predict a spatially varying gain map for reliable dynamic-range expansion. We evaluate on Kalantari, Tel, and Challenge123 using both full-reference and no-reference metrics, where DOME-HDR achieves state-of-the-art HDR reconstruction quality; ablations further confirm the effectiveness of dual cross-attention and SDR-guided gain map estimation.
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