pwc-archive/papers-with-abstracts huggingface dataset:
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Authors: Xin Li, Yeying Jin, Xin Jin, Zongwei Wu, Bingchen Li, YuFei Wang, Wenhan Yang, Yu Li, Zhibo Chen, Bihan Wen, Robby T. Tan, Radu Timofte, Qiyu Rong, Hongyuan Jing, Mengmeng Zhang, Jinglong Li, Xiangyu Lu, Yi Ren, YuTing Liu, Meng Zhang, Xiang Chen, Qiyuan Guan, Jiangxin Dong, Jinshan Pan, Conglin Gou, Qirui Yang, Fangpu Zhang, Yunlong Lin, Sixiang Chen, Guoxi Huang, Ruirui Lin, Yan Zhang, Jingyu Yang, Huanjing Yue, Jiyuan Chen, Qiaosi Yi, Hongjun Wang, Chenxi Xie, Shuai Li, Yuhui Wu, Kaiyi Ma, Jiakui Hu, Juncheng Li, Liwen Pan, Guangwei Gao, Wenjie Li, Zhenyu Jin, Heng Guo, Zhanyu Ma, YuBo Wang, Jinghua Wang, Wangzhi Xing, Anjusree Karnavar, Diqi Chen, Mohammad Aminul Islam, Hao Yang, Ruikun Zhang, Liyuan Pan, Qianhao Luo, XinCao, Han Zhou, Yan Min, Wei Dong, Jun Chen, Taoyi Wu, Weijia Dou, Yu Wang, Shengjie Zhao, Yongcheng Huang, Xingyu Han, Anyan Huang, Hongtao Wu, Hong Wang, Yefeng Zheng, Abhijeet Kumar, Aman Kumar, Marcos V. Conde, Paula Garrido, Daniel Feijoo, Juan C. Benito, Guanglu Dong, Xin Lin, Siyuan Liu, Tianheng Zheng, Jiayu Zhong, Shouyi Wang, Xiangtai Li, Lanqing Guo, Lu Qi, Chao Ren, Shuaibo Wang, Shilong Zhang, Wanyu Zhou, Yunze Wu, Qinzhong Tan, Jieyuan Pei, Zhuoxuan Li, Jiayu Wang, Haoyu Bian, Haoran Sun, Subhajit Paul, Ni Tang, Junhao Huang, Zihan Cheng, Hongyun Zhu, Yuehan Wu, Kaixin Deng, Hang Ouyang, Tianxin Xiao, Fan Yang, Zhizun Luo, Zeyu Xiao, Zhuoyuan Li, Nguyen Pham Hoang Le, An Dinh Thien, Son T. Luu, Kiet Van Nguyen, Ronghua Xu, Xianmin Tian, Weijian Zhou, Jiacheng Zhang, Yuqian Chen, Yihang Duan, Yujie Wu, Suresh Raikwar, Arsh Garg, Kritika, Jianhua Zheng, Xiaoshan Ma, Ruolin Zhao, Yongyu Yang, Yongsheng Liang, Guiming Huang, Qiang Li, Hongbin Zhang, Xiangyu Zheng, A. N. Rajagopalan
Date: 2025-04-17 00:00:00
Tasks: Raindrop Removal, Rain Removal, valid

This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset. Unlike existing deraining datasets, our Raindrop Clarity dataset is more diverse and challenging in degradation types and contents, which includes day raindrop-focused, day background-focused, night raindrop-focused, and night background-focused degradations. This dataset is divided into three subsets for competition: 14,139 images for training, 240 images for validation, and 731 images for testing. The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions. There are a total of 361 participants in the competition, and 32 teams submitting valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the Raindrop Clarity dataset. The project can be found at https://lixinustc.github.io/CVPR-NTIRE2025-RainDrop-Competition.github.io/.
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification
Authors: Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen, Geoffrey Eappen, Prabhu Thiruvasagam, Hong-Fu Chou, Duc-Dung Tran, Hung Nguyen-Kha, Luis M. Garces-Socarras, Jorge L. Gonzalez-Rios, Juan Carlos Merlano-Duncan, Symeon Chatzinotas
Date: 2025-04-16 00:00:00
Tasks: Computational Efficiency, Earth Observation, image-classification, Image Classification

This study introduces ResNet-GLUSE, a lightweight ResNet variant enhanced with Gated Linear Unit-enhanced Squeeze-and-Excitation (GLUSE), an adaptive channel-wise attention mechanism. By integrating dynamic gating into the traditional SE framework, GLUSE improves feature recalibration while maintaining computational efficiency. Experiments on EuroSAT and PatternNet datasets confirm its effectiveness, achieving exceeding \textbf{94\% and 98\% accuracy}, respectively. While \textbf{MobileViT achieves 99\% accuracy}, ResNet-GLUSE offers \textbf{33x fewer parameters, 27x fewer FLOPs, 33x smaller model size (MB), $\approx$6x lower power consumption (W), and $\approx$3x faster inference time (s)}, making it significantly more efficient for onboard satellite deployment. Furthermore, due to its simplicity, ResNet-GLUSE can be easily mimicked for \textbf{neuromorphic computing}, enabling ultra-low power inference at just \textbf{852.30 mW} on Akida Brainchip. This balance between high accuracy and ultra-low resource consumption establishes ResNet-GLUSE as a practical solution for real-time Earth Observation (EO) tasks. Reproducible codes are available in our shared repository.
Correlation Ratio for Unsupervised Learning of Multi-modal Deformable Registration
Authors: Xiaojian Chen, Yihao Liu, Shuwen Wei, Aaron Carass, Yong Du, Junyu Chen
Date: 2025-04-16 00:00:00
Tasks: Image Registration

In recent years, unsupervised learning for deformable image registration has been a major research focus. This approach involves training a registration network using pairs of moving and fixed images, along with a loss function that combines an image similarity measure and deformation regularization. For multi-modal image registration tasks, the correlation ratio has been a widely-used image similarity measure historically, yet it has been underexplored in current deep learning methods. Here, we propose a differentiable correlation ratio to use as a loss function for learning-based multi-modal deformable image registration. This approach extends the traditionally non-differentiable implementation of the correlation ratio by using the Parzen windowing approximation, enabling backpropagation with deep neural networks. We validated the proposed correlation ratio on a multi-modal neuroimaging dataset. In addition, we established a Bayesian training framework to study how the trade-off between the deformation regularizer and similarity measures, including mutual information and our proposed correlation ratio, affects the registration performance.
Human Aligned Compression for Robust Models
Authors: Samuel Räber, Andreas Plesner, Till Aczel, Roger Wattenhofer
Date: 2025-04-16 00:00:00

Adversarial attacks on image models threaten system robustness by introducing imperceptible perturbations that cause incorrect predictions. We investigate human-aligned learned lossy compression as a defense mechanism, comparing two learned models (HiFiC and ELIC) against traditional JPEG across various quality levels. Our experiments on ImageNet subsets demonstrate that learned compression methods outperform JPEG, particularly for Vision Transformer architectures, by preserving semantically meaningful content while removing adversarial noise. Even in white-box settings where attackers can access the defense, these methods maintain substantial effectiveness. We also show that sequential compression--applying rounds of compression/decompression--significantly enhances defense efficacy while maintaining classification performance. Our findings reveal that human-aligned compression provides an effective, computationally efficient defense that protects the image features most relevant to human and machine understanding. It offers a practical approach to improving model robustness against adversarial threats.
Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
Authors: Zhijin He, Alan B. McMillan
Date: 2025-04-16 00:00:00
Tasks: Deep Learning, Diagnostic

The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography, focusing on COVID-19, lung opacity, and viral pneumonia. While deep learning models, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), learn directly from image data, radiomics-based models extract and analyze quantitative features, potentially providing advantages in data-limited scenarios. This study systematically compares the diagnostic accuracy and robustness of various AI models, including Decision Trees, Gradient Boosting, Random Forests, Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP) for radiomics, against state-of-the-art computer vision deep learning architectures. Performance metrics across varying sample sizes reveal insights into each model's efficacy, highlighting the contexts in which specific AI approaches may offer enhanced diagnostic capabilities. The results aim to inform the integration of AI-driven diagnostic tools in clinical practice, particularly in automated and high-throughput environments where timely, reliable diagnosis is critical. This comparative study addresses an essential gap, establishing guidance for the selection of AI models based on clinical and operational needs.
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction
Authors: Xia Wang, Haiyang Sun, Tiantian Cao, Yueying Sun, Min Feng
Date: 2025-04-16 00:00:00
Tasks: Decoder, Image Generation, Self-Supervised Learning

Moir\'e patterns, resulting from aliasing between object light signals and camera sampling frequencies, often degrade image quality during capture. Traditional demoir\'eing methods have generally treated images as a whole for processing and training, neglecting the unique signal characteristics of different color channels. Moreover, the randomness and variability of moir\'e pattern generation pose challenges to the robustness of existing methods when applied to real-world data. To address these issues, this paper presents SIDME (Self-supervised Image Demoir\'eing via Masked Encoder-Decoder Reconstruction), a novel model designed to generate high-quality visual images by effectively processing moir\'e patterns. SIDME combines a masked encoder-decoder architecture with self-supervised learning, allowing the model to reconstruct images using the inherent properties of camera sampling frequencies. A key innovation is the random masked image reconstructor, which utilizes an encoder-decoder structure to handle the reconstruction task. Furthermore, since the green channel in camera sampling has a higher sampling frequency compared to red and blue channels, a specialized self-supervised loss function is designed to improve the training efficiency and effectiveness. To ensure the generalization ability of the model, a self-supervised moir\'e image generation method has been developed to produce a dataset that closely mimics real-world conditions. Extensive experiments demonstrate that SIDME outperforms existing methods in processing real moir\'e pattern data, showing its superior generalization performance and robustness.
Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders
Authors: Levente Lippenszky, István Megyeri, Krisztian Koos, Zsófia Karancsi, Borbála Deák-Karancsi, András Frontó, Árpád Makk, Attila Rádics, Erhan Bas, László Ruskó
Date: 2025-04-16 00:00:00
Tasks: Denoising

In radiation therapy planning, inaccurate segmentations of organs at risk can result in suboptimal treatment delivery, if left undetected by the clinician. To address this challenge, we developed a denoising autoencoder-based method to detect inaccurate organ segmentations. We applied noise to ground truth organ segmentations, and the autoencoders were tasked to denoise them. Through the application of our method to organ segmentations generated on both MR and CT scans, we demonstrated that the method is independent of imaging modality. By providing reconstructions, our method offers visual information about inaccurate regions of the organ segmentations, leading to more explainable detection of suboptimal segmentations. We compared our method to existing approaches in the literature and demonstrated that it achieved superior performance for the majority of organs.
Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline
Authors: Joanne Lin, Crispian Morris, Ruirui Lin, Fan Zhang, David Bull, Nantheera Anantrasirichai
Date: 2025-04-16 00:00:00
Tasks: object-detection, Object Detection, Video Enhancement

Low-light conditions pose significant challenges for both human and machine annotation. This in turn has led to a lack of research into machine understanding for low-light images and (in particular) videos. A common approach is to apply annotations obtained from high quality datasets to synthetically created low light versions. In addition, these approaches are often limited through the use of unrealistic noise models. In this paper, we propose a new Degradation Estimation Network (DEN), which synthetically generates realistic standard RGB (sRGB) noise without the requirement for camera metadata. This is achieved by estimating the parameters of physics-informed noise distributions, trained in a self-supervised manner. This zero-shot approach allows our method to generate synthetic noisy content with a diverse range of realistic noise characteristics, unlike other methods which focus on recreating the noise characteristics of the training data. We evaluate our proposed synthetic pipeline using various methods trained on its synthetic data for typical low-light tasks including synthetic noise replication, video enhancement, and object detection, showing improvements of up to 24\% KLD, 21\% LPIPS, and 62\% AP$_{50-95}$, respectively.
A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction
Authors: Zhenyu Yu, Mohd Yamani Inda Idris, Pei Wang
Date: 2025-04-16 00:00:00
Tasks: Disaster Response, Image Reconstruction, Management

Remote sensing imagery is essential for environmental monitoring, agricultural management, and disaster response. However, data loss due to cloud cover, sensor failures, or incomplete acquisition-especially in high-resolution and high-frequency tasks-severely limits satellite imagery's effectiveness. Traditional interpolation methods struggle with large missing areas and complex structures. Remote sensing imagery consists of multiple bands, each with distinct meanings, and ensuring consistency across bands is critical to avoid anomalies in the combined images. This paper proposes SatelliteMaker, a diffusion-based method that reconstructs missing data across varying levels of data loss while maintaining spatial, spectral, and temporal consistency. We also propose Digital Elevation Model (DEM) as a conditioning input and use tailored prompts to generate realistic images, making diffusion models applicable to quantitative remote sensing tasks. Additionally, we propose a VGG-Adapter module based on Distribution Loss, which reduces distribution discrepancy and ensures style consistency. Extensive experiments show that SatelliteMaker achieves state-of-the-art performance across multiple tasks.
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning
Authors: Daiqi Liu, Fuxin Fan, Andreas Maier
Date: 2025-04-16 00:00:00
Tasks: Anatomy, Decision Making, Image Generation

X-ray imaging plays a crucial role in the medical field, providing essential insights into the internal anatomy of patients for diagnostics, image-guided procedures, and clinical decision-making. Traditional techniques often require multiple X-ray projections from various angles to obtain a comprehensive view, leading to increased radiation exposure and more complex clinical processes. This paper explores an innovative approach using the DL-GIPS model, which synthesizes X-ray projections from new viewpoints by leveraging a single existing projection. The model strategically manipulates geometry and texture features extracted from an initial projection to match new viewing angles. It then synthesizes the final projection by merging these modified geometry features with consistent texture information through an advanced image generation process. We demonstrate the effectiveness and broad applicability of the DL-GIPS framework through lung imaging examples, highlighting its potential to revolutionize stereoscopic and volumetric imaging by minimizing the need for extensive data acquisition.
TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation
Authors: Kangbo Ma
Date: 2025-04-16 00:00:00
Tasks: Image Segmentation, Latent Diffusion Model for 3D, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Diffusion Probabilistic Models (DPMs) have demonstrated significant potential in 3D medical image segmentation tasks. However, their high computational cost and inability to fully capture global 3D contextual information limit their practical applications. To address these challenges, we propose a novel text-guided diffusion model framework, TextDiffSeg. This method leverages a conditional diffusion framework that integrates 3D volumetric data with natural language descriptions, enabling cross-modal embedding and establishing a shared semantic space between visual and textual modalities. By enhancing the model's ability to recognize complex anatomical structures, TextDiffSeg incorporates innovative label embedding techniques and cross-modal attention mechanisms, effectively reducing computational complexity while preserving global 3D contextual integrity. Experimental results demonstrate that TextDiffSeg consistently outperforms existing methods in segmentation tasks involving kidney and pancreas tumors, as well as multi-organ segmentation scenarios. Ablation studies further validate the effectiveness of key components, highlighting the synergistic interaction between text fusion, image feature extractor, and label encoder. TextDiffSeg provides an efficient and accurate solution for 3D medical image segmentation, showcasing its broad applicability in clinical diagnosis and treatment planning.
HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration
Authors: Chia-Hsiang Lin, Si-Sheng Young
Date: 2025-04-16 00:00:00
Tasks: blind source separation, Image Restoration

Quantum machine intelligence starts showing its impact on satellite remote sensing (SRS). Also, recent literature exhibits that quantum generative intelligences encompass superior potential than their classical counterpart, motivating us to develop quantum generative adversarial networks (GANs) for SRS. However, existing quantum GANs are restricted by the limited quantum bit (qubit) resources of current quantum computers and process merely a small 2x2 grayscale image, far from being applicable to SRS. Recently, the novel concept of hybrid quantum-classical GAN, a quantum generator with a classical discriminator, has upgraded the order to 28x28 (still grayscale), whereas it is still insufficient for SRS. This motivates us to design a radically new hybrid framework, where both generator and discriminator are hybrid architectures. We demonstrate this feasibility, leading to a breakthrough of processing 128x128 hyperspectral images for SRS. Specifically, we design the quantum part with mathematically provable quantum full expressibility (FE) to address core signal processing tasks, wherein the FE property allows the quantum network to realize any valid quantum operator with appropriate training. The classical part, composed of convolutional layers, treats the read-in (compressing the optical information into limited qubits) and read-out (addressing the quantum collapse effect) procedures. The proposed innovative hybrid quantum GAN, named Hyperspectral Knot-like IntelligeNt dIscrimiNator and Generator (HyperKING), where knot partly symbolizes the quantum entanglement and partly the compressed quantum domain in the central part of the network architecture. HyperKING significantly surpasses the classical approaches in hyperspectral tensor completion, mixed noise removal (about 3dB improvement), and blind source separation results.
Regist3R: Incremental Registration with Stereo Foundation Model
Authors: Sidun Liu, Wenyu Li, Peng Qiao, Yong Dou
Date: 2025-04-16 00:00:00
Tasks: 3D Reconstruction, Camera Pose Estimation, Computational Efficiency, model, Multi-View 3D Reconstruction, Pose Estimation

Multi-view 3D reconstruction has remained an essential yet challenging problem in the field of computer vision. While DUSt3R and its successors have achieved breakthroughs in 3D reconstruction from unposed images, these methods exhibit significant limitations when scaling to multi-view scenarios, including high computational cost and cumulative error induced by global alignment. To address these challenges, we propose Regist3R, a novel stereo foundation model tailored for efficient and scalable incremental reconstruction. Regist3R leverages an incremental reconstruction paradigm, enabling large-scale 3D reconstructions from unordered and many-view image collections. We evaluate Regist3R on public datasets for camera pose estimation and 3D reconstruction. Our experiments demonstrate that Regist3R achieves comparable performance with optimization-based methods while significantly improving computational efficiency, and outperforms existing multi-view reconstruction models. Furthermore, to assess its performance in real-world applications, we introduce a challenging oblique aerial dataset which has long spatial spans and hundreds of views. The results highlight the effectiveness of Regist3R. We also demonstrate the first attempt to reconstruct large-scale scenes encompassing over thousands of views through pointmap-based foundation models, showcasing its potential for practical applications in large-scale 3D reconstruction tasks, including urban modeling, aerial mapping, and beyond.
WaterFlow: Learning Fast & Robust Watermarks using Stable Diffusion
Authors: Vinay Shukla, Prachee Sharma, Ryan Rossi, Sungchul Kim, Tong Yu, Aditya Grover
Date: 2025-04-15 00:00:00

The ability to embed watermarks in images is a fundamental problem of interest for computer vision, and is exacerbated by the rapid rise of generated imagery in recent times. Current state-of-the-art techniques suffer from computational and statistical challenges such as the slow execution speed for practical deployments. In addition, other works trade off fast watermarking speeds but suffer greatly in their robustness or perceptual quality. In this work, we propose WaterFlow (WF), a fast and extremely robust approach for high fidelity visual watermarking based on a learned latent-dependent watermark. Our approach utilizes a pretrained latent diffusion model to encode an arbitrary image into a latent space and produces a learned watermark that is then planted into the Fourier Domain of the latent. The transformation is specified via invertible flow layers that enhance the expressivity of the latent space of the pre-trained model to better preserve image quality while permitting robust and tractable detection. Most notably, WaterFlow demonstrates state-of-the-art performance on general robustness and is the first method capable of effectively defending against difficult combination attacks. We validate our findings on three widely used real and generated datasets: MS-COCO, DiffusionDB, and WikiArt.