We introduced 2 novel datasets for image dehazing. The NH-Haze2 and DNH-HAZE datasets have been used to NTIRE CVPR image dehazing challenges to gauge the state-of-the-art in image dehazing.
Related to our new image dehazing prior the qualitative and quantitative evaluations demonstrate that our approach yields better results than previous physically-based image dehazing techniques, and favourably compares with the deep learning dehazing approaches.
Moreover, building on the recent results presented in our NTIRE challenge reports and the methods explored in the previous phase, we designed a novel deep learning approach based on Convolutional Neural Networks (CNNs) for image dehazing. We conducted a comprehensive study to evaluate the effectiveness of various loss functions for image dehazing. We selected two representative models with distinct architectures and design philosophies: AOD-Net and UVM-Net.
We present a study on evaluating image dehazing methods through feature-level analysis using the SIFT operator, motivated by the premise that dehazing enhances local feature visibility and consistency—key for improving CNN robustness in real-world hazy conditions. Using the NH-HAZE2 dataset, we assessed several dehazing techniques, including our CNN-based approach, by measuring the number of correct SIFT feature matches between dehazed outputs and corresponding ground truth images.
We propose a multimodal learning approach to image dehazing, framing the task as a modality translation problem that transforms hazy inputs into representations interpretable by models trained on clean data. Rather than adapting interpretation models to degraded inputs, our method uses an image-to-image CNN supervised by interpretation accuracy to align semantic understanding with visual restoration.
This project will have a significant long-term impact on both the researcher’s career and the academic ecosystem. By engaging with cutting-edge research in deep learning and inverse problems at a leading host institution, the researcher has gained in advanced scientific knowledge and interdisciplinary expertise that perfectly complement his academic profile. Exposure to ongoing high-level projects not only deepens his technical competence but also enhance his understanding of how different research domains interconnect. Upon returning to UPT for a full-time tenure-track position, the researcher will be well-positioned to introduce novel research directions, foster international collaborations, and enrich the curriculum with state-of-the-art content, thereby contributing to the strategic development of his home institution in the field of AI and image processing.
The innovation capacity of the host institution was significantly enhanced through the multifaceted achievements of this project, which advanced both foundational research and applied methodologies in image dehazing. For instance, the development and public release of two high-quality image dehazing datasets have positioned the host as a key contributor to global benchmarking efforts, with both datasets now integrated into the NTIRE CVPR challenges. These resources not only support the broader research community but also strengthen the institution's visibility in the computer vision community
