Ultra-High-Definition Dynamic Multi-Exposure Image Fusion via Infinite Pixel Learning
Authors: Xingchi Chen, Zhuoran Zheng, Xuerui Li, Yuying Chen, Shu Wang, Wenqi Ren
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that our method maintains high-quality visual performance while fusing UHD dynamic multi-exposure images in real-time (>40fps) on a single consumer-grade GPU. Our main contributions are summarized as follows: ... We introduce 4K-DMEF, the first UHD MEF benchmark dataset for dynamic scenes. Our method, tested on both this UHD dataset and other non-UHD datasets, achieves a balance between performance and efficiency. Experiments In this section, we evaluate the proposed method by conducting comprehensive experiments on both our UHD dataset and several public non-UHD datasets. We compare our method against five state-of-the-art multi-exposure fusion (MEF) methods... In addition, we conduct ablation studies to show the effectiveness of each module within our network. |
| Researcher Affiliation | Academia | 1Shenzhen Campus of Sun Yat-sen University 2Jimei University 3The State University of New York at Buffalo 4Fuzhou University EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using prose, mathematical equations (1) through (15), and block diagrams (Figure 2). There are no explicitly labeled pseudocode or algorithm blocks with structured, code-like steps. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code, a link to a code repository, or mention of code in supplementary materials. |
| Open Datasets | Yes | Our main contributions are summarized as follows: ... We introduce 4K-DMEF, the first UHD MEF benchmark dataset for dynamic scenes. ... Non-UHD Dataset. We also conduct experiments using two public non-UHD dynamic multi-exposure image datasets: Kalantari Dataset (Kalantari and Ramamoorthi 2017) and Mobile-HDR Dataset (Liu et al. 2023). |
| Dataset Splits | Yes | Our UHD Dynamic Multi-Exposure Image Dataset. Ultimately, we collected data for 110 UHD dynamic scenes, dividing them into 80 scenes for training and 30 for testing. ... Non-UHD Dataset. Kalantari Dataset (Kalantari and Ramamoorthi 2017) ... includes 74 training scenes and 15 test scenes. Mobile-HDR Dataset (Liu et al. 2023) ... We divide this dataset into 85 training scenes and 30 test scenes, each with a resolution of 2000 1500 pixels. |
| Hardware Specification | Yes | Implementation Details We conduct our experiments using Py Torch on a single NVIDIA GeForce RTX 4090 GPU. ... Table 1: Comparison of quantitative results on our 4K-DMEF datasets. MR denotes the maximum resolution each algorithm can handle on a single RTX 4090 GPU. |
| Software Dependencies | No | We conduct our experiments using Py Torch on a single NVIDIA GeForce RTX 4090 GPU. To optimize the network, we employ the Adam W optimizer with a learning rate 2 10 4. The paper mentions PyTorch and the AdamW optimizer but does not specify their version numbers, which is required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | Implementation Details We conduct our experiments using Py Torch on a single NVIDIA GeForce RTX 4090 GPU. To optimize the network, we employ the Adam W optimizer with a learning rate 2 10 4. The network undergoes training for 1200 epochs with a batch size of 4. Additionally, the number of Feature Integration Blocks (FIBs) is 8, and the number of feature channels is 48. |