Daniel Puiu Poenar

Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus et al.

Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And Contrastive DehazeNet, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts. A major innovation is its ability to train effectively with limited data, achieved through a novel quadruplet loss-based contrastive dehazing paradigm. This approach distinctly separates hazy and clear image features while also distinguish lower-quality and higher-quality dehazed images obtained from each sub-modules of our network, thereby refining the dehazing process to a larger extent. Extensive tests on a variety of benchmarked haze datasets demonstrated the superiority of our approach. The code repository for this work is available at https://github.com/GreedYLearner1146/DRACO-DehazeNet.

Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus et al.

Few-Shot Learning (FSL), which involves learning to generalize using only a few data samples, has demonstrated promising and superior performances to ordinary CNN methods. While Bayesian based estimation approaches using Kullback-Leibler (KL) divergence have shown improvements, they remain vulnerable to adversarial attacks and natural noises. We introduce ANROT-HELANet, an Adversarially and Naturally RObusT Hellinger Aggregation Network that significantly advances the state-of-the-art in FSL robustness and performance. Our approach implements an adversarially and naturally robust Hellinger distance-based feature class aggregation scheme, demonstrating resilience to adversarial perturbations up to $ε=0.30$ and Gaussian noise up to $σ=0.30$. The network achieves substantial improvements across benchmark datasets, including gains of 1.20\% and 1.40\% for 1-shot and 5-shot scenarios on miniImageNet respectively. We introduce a novel Hellinger Similarity contrastive loss function that generalizes cosine similarity contrastive loss for variational few-shot inference scenarios. Our approach also achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE (3.43) and WAE (3.38) approaches. Extensive experiments conducted on four few-shot benchmarked datasets verify that ANROT-HELANet's combination of Hellinger distance-based feature aggregation, attention mechanisms, and our novel loss function establishes new state-of-the-art performance while maintaining robustness against both adversarial and natural perturbations. Our code repository will be available at https://github.com/GreedYLearner1146/ANROT-HELANet/tree/main.

Gao Yu Lee, Jinkuan Chen, Tanmoy Dam et al.

High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Since the introduction of deep convolutional neural networks, numerous approaches have been proposed, and even more have emerged with the development of vision transformers and contrastive/few-shot learning. Simultaneously, papers describing dehazing architectures applicable to various Remote Sensing (RS) domains are also being published. This review goes beyond the traditional focus on benchmarked haze datasets, as we also explore the application of dehazing techniques to remote sensing and UAV datasets, providing a comprehensive overview of both deep learning and prior-based approaches in these domains. We identify key challenges, including the lack of large-scale RS datasets and the need for more robust evaluation metrics, and outline potential solutions and future research directions to address them. This review is the first, to our knowledge, to provide comprehensive discussions on both existing and very recent dehazing approaches (as of 2024) on benchmarked and RS datasets, including UAV-based imagery.

Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus et al.

The increasing frequency of natural and human-induced disasters necessitates advanced visual recognition techniques capable of analyzing critical photographic data. With progress in artificial intelligence and resilient computational systems, rapid and accurate disaster classification has become crucial for efficient rescue operations. However, visual recognition in disaster contexts faces significant challenges due to limited and diverse data from the difficulties in collecting and curating comprehensive, high-quality disaster imagery. Few-Shot Learning (FSL) provides a promising approach to data scarcity, yet current FSL research mainly relies on generic benchmark datasets lacking remote-sensing disaster imagery, limiting its practical effectiveness. Moreover, disaster images exhibit high intra-class variation and inter-class similarity, hindering the performance of conventional metric-based FSL methods. To address these issues, this paper introduces the Attention-based Bhattacharyya-Hellinger Feature Aggregation Network (ATTBHFA-Net), which linearly combines the Bhattacharyya coefficient and Hellinger distances to compare and aggregate feature probability distributions for robust prototype formation. The Bhattacharyya coefficient serves as a contrastive margin that enhances inter-class separability, while the Hellinger distance regularizes same-class alignment. This framework parallels contrastive learning but operates over probability distributions rather than embedded feature points. Furthermore, a Bhattacharyya-Hellinger distance-based contrastive loss is proposed as a distributional counterpart to cosine similarity loss, used jointly with categorical cross-entropy to significantly improve FSL performance. Experiments on four FSL benchmarks and two disaster image datasets demonstrate the superior effectiveness and generalization of ATTBHFA-Net compared to existing approaches.