pwc-archive/papers-with-abstracts huggingface dataset:
Low-Dose CT Image Enhancement Using Deep Learning
Authors: A. Demir, M. M. A. Shames, O. N. Gerek, S. Ergin, M. Fidan, M. Koc, M. B. Gulmezoglu, A. Barkana, C. Calisir
Date: 2023-10-31 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Diagnostic, Image Enhancement, Image Reconstruction

The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are performed for the reconstruction of slices of body tissues. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique, which reduces the x-ray dose but can cause a loss of image sharpness. Since CT image reconstruction from directional x-rays is a nonlinear process, it is analytically difficult to correct the effect of dose reduction on image quality. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts. Some recent works propose combinations of multiple deep-learning and classical methods for this purpose, which over-complicate the process. However, it is observed here that the straight utilization of the well-known U-NET provides very successful results for the correction of low-dose artifacts. Blind tests with actual radiologists reveal that the U-NET enhanced quarter-dose CT images not only provide an immense visual improvement over the low-dose versions, but also become diagnostically preferable images, even when compared to their full-dose CT versions.
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Authors: Qingqing Zhao, Peizhuo Li, Wang Yifan, Olga Sorkine-Hornung, Gordon Wetzstein
Date: 2023-10-31 00:00:00
Tasks: motion retargeting, Motion Synthesis

Creating believable motions for various characters has long been a goal in computer graphics. Current learning-based motion synthesis methods depend on extensive motion datasets, which are often challenging, if not impossible, to obtain. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we utilize this alternative data source and introduce a neural motion synthesis approach through retargeting. Our method generates plausible motions for characters that have only pose data by transferring motion from an existing motion capture dataset of another character, which can have drastically different skeletons. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Project page: https://cyanzhao42.github.io/pose2motion
Contrast-agent-induced deterministic component of CT-density in the abdominal aorta during routine angiography: proof of concept study
Authors: Maria R. Kodenko, Yuriy A. Vasilev, Nicholas S. Kulberg, Andrey V. Samorodov, Anton V. Vladzimirskyy, Olga V. Omelyanskaya, Roman V. Reshetnikov
Date: 2023-10-31 00:00:00
Tasks: Diagnostic

Background and objective: CTA is a gold standard of preoperative diagnosis of abdominal aorta and typically used for geometric-only characteristic extraction. We assume that a model describing the dynamic behavior of the contrast agent in the vessel can be developed from the data of routine CTA studies, allowing the procedure to be investigated and optimized without the need for additional perfusion CT studies. Obtained spatial distribution of CA can be valuable for both increasing the diagnostic value of a particular study and improving the CT data processing tools. Methods: In accordance with the Beer-Lambert law and the absence of chemical interaction between blood and CA, we postulated the existence of a deterministic CA-induced component in the CT signal density. The proposed model, having a double-sigmoid structure, contains six coefficients relevant to the properties of hemodynamics. To validate the model, expert segmentation was performed using the 3D Slicer application for the CTA data obtained from publicly available source. The model was fitted to the data using the non-linear least square method with Levenberg-Marquardt optimization. Results: We analyzed 594 CTA images (4 studies with median size of 144 slices, IQR [134; 158.5]; 1:1 normal:pathology balance). Goodness-of-fit was proved by Wilcox test (p-value > 0.05 for all cases). The proposed model correctly simulated normal blood flow and hemodynamics disturbances caused by local abnormalities (aneurysm, thrombus and arterial branching). Conclusions: Proposed approach can be useful for personalized CA modeling of vessels, improvement of CTA image processing and preparation of synthetic CT training data for artificial intelligence.
UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer
Authors: Weiwen Chen, Yingtie Lei, Shenghong Luo, Ziyang Zhou, Mingxian Li, Chi-Man Pun
Date: 2023-10-31 00:00:00
Tasks: Image Enhancement

Underwater images often exhibit poor quality, distorted color balance and low contrast due to the complex and intricate interplay of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) The current deep learning methods rely on Convolutional Neural Networks (CNNs) that lack the multi-scale enhancement, and global perception field is also limited. (ii) The scarcity of paired real-world underwater datasets poses a significant challenge, and the utilization of synthetic image pairs could lead to overfitting. To address the aforementioned problems, this paper introduces a Multi-scale Transformer-based Network called UWFormer for enhancing images at multiple frequencies via semi-supervised learning, in which we propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale Fusion Feed-forward Network for low-frequency enhancement. Besides, we introduce a special underwater semi-supervised training strategy, where we propose a Subaqueous Perceptual Loss function to generate reliable pseudo labels. Experiments using full-reference and non-reference underwater benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of both quantity and visual quality.
ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
Authors: Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, Huchuan Lu
Date: 2023-10-31 00:00:00
Tasks: Camouflaged Object Segmentation

Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To this end, we propose an effective unified collaborative pyramid network that mimics human behavior when observing vague images and videos, \ie zooming in and out. Specifically, our approach employs the zooming strategy to learn discriminative mixed-scale semantics by the multi-head scale integration and rich granularity perception units, which are designed to fully explore imperceptible clues between candidate objects and background surroundings. The former's intrinsic multi-head aggregation provides more diverse visual patterns. The latter's routing mechanism can effectively propagate inter-frame differences in spatiotemporal scenarios and be adaptively deactivated and output all-zero results for static representations. They provide a solid foundation for realizing a unified architecture for static and dynamic COD. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization, uncertainty awareness loss, to encourage predictions with higher confidence in candidate regions. Our highly task-friendly framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks. Our code can be found at {https://github.com/lartpang/ZoomNeXt}.
Visible to Thermal image Translation for improving visual task in low light conditions
Authors: Md Azim Khan
Date: 2023-10-31 00:00:00
Tasks: Image-to-Image Translation, Pedestrian Detection

Several visual tasks, such as pedestrian detection and image-to-image translation, are challenging to accomplish in low light using RGB images. Heat variation of objects in thermal images can be used to overcome this. In this work, an end-to-end framework, which consists of a generative network and a detector network, is proposed to translate RGB image into Thermal ones and compare generated thermal images with real data. We have collected images from two different locations using the Parrot Anafi Thermal drone. After that, we created a two-stream network, preprocessed, augmented, the image data, and trained the generator and discriminator models from scratch. The findings demonstrate that it is feasible to translate RGB training data to thermal data using GAN. As a result, thermal data can now be produced more quickly and affordably, which is useful for security and surveillance applications.
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations
Authors: Kunyu Wang, Juluan Shi, Wenxuan Wang
Date: 2023-10-31 00:00:00
Tasks: Adversarial Attack

Deep neural networks are susceptible to adversarial attacks, which pose a significant threat to their security and reliability in real-world applications. The most notable adversarial attacks are transfer-based attacks, where an adversary crafts an adversarial example to fool one model, which can also fool other models. While previous research has made progress in improving the transferability of untargeted adversarial examples, the generation of targeted adversarial examples that can transfer between models remains a challenging task. In this work, we present a novel approach to generate transferable targeted adversarial examples by exploiting the vulnerability of deep neural networks to perturbations on high-frequency components of images. We observe that replacing the high-frequency component of an image with that of another image can mislead deep models, motivating us to craft perturbations containing high-frequency information to achieve targeted attacks. To this end, we propose a method called Low-Frequency Adversarial Attack (\name), which trains a conditional generator to generate targeted adversarial perturbations that are then added to the low-frequency component of the image. Extensive experiments on ImageNet demonstrate that our proposed approach significantly outperforms state-of-the-art methods, improving targeted attack success rates by a margin from 3.2\% to 15.5\%.
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
Authors: Reza Basiri, Karim Manji, Francois Harton, Alisha Poonja, Milos R. Popovic, Shehroz S. Khan
Date: 2023-10-31 00:00:00
Tasks: model

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emerged as powerful tools for generating synthetic images with remarkable realism and diversity in many applications. This paper explores the potential of diffusion models for synthesizing DFU images and evaluates their authenticity through expert clinician assessments. Additionally, evaluation metrics such as Frechet Inception Distance (FID) and Kernel Inception Distance (KID) are examined to assess the quality of the synthetic DFU images. A dataset of 2,000 DFU images is used for training the diffusion model, and the synthetic images are generated by applying diffusion processes. The results indicate that the diffusion model successfully synthesizes visually indistinguishable DFU images. 70% of the time, clinicians marked synthetic DFU images as real DFUs. However, clinicians demonstrate higher unanimous confidence in rating real images than synthetic ones. The study also reveals that FID and KID metrics do not significantly align with clinicians' assessments, suggesting alternative evaluation approaches are needed. The findings highlight the potential of diffusion models for generating synthetic DFU images and their impact on medical training programs and research in wound detection and classification.
Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023
Authors: Fen Fang, Yi Cheng, Ying Sun, Qianli Xu
Date: 2023-10-31 00:00:00
Tasks: Hand Segmentation, Object, Segmentation, Semantic Segmentation

In this report, we present our approach to the EPIC-KITCHENS VISOR Hand Object Segmentation Challenge, which focuses on the estimation of the relation between the hands and the objects given a single frame as input. The EPIC-KITCHENS VISOR dataset provides pixel-wise annotations and serves as a benchmark for hand and active object segmentation in egocentric video. Our approach combines the baseline method, i.e., Point-based Rendering (PointRend) and the Segment Anything Model (SAM), aiming to enhance the accuracy of hand and object segmentation outcomes, while also minimizing instances of missed detection. We leverage accurate hand segmentation maps obtained from the baseline method to extract more precise hand and in-contact object segments. We utilize the class-agnostic segmentation provided by SAM and apply specific hand-crafted constraints to enhance the results. In cases where the baseline model misses the detection of hands or objects, we re-train an object detector on the training set to enhance the detection accuracy. The detected hand and in-contact object bounding boxes are then used as prompts to extract their respective segments from the output of SAM. By effectively combining the strengths of existing methods and applying our refinements, our submission achieved the 1st place in terms of evaluation criteria in the VISOR HOS Challenge.
Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery
Authors: Hong Danyang, Yu Anzhu, Ji Song, Cao Xuefeng, Quan Yujun, Guo Wenyue, Qiu Chunping
Date: 2023-10-31 00:00:00
Tasks: 3D Reconstruction, Surface Reconstruction

In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.
Medical Image Denosing via Explainable AI Feature Preserving Loss
Authors: Guanfang Dong, Anup Basu
Date: 2023-10-31 00:00:00
Tasks: Denoising, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Denoising algorithms play a crucial role in medical image processing and analysis. However, classical denoising algorithms often ignore explanatory and critical medical features preservation, which may lead to misdiagnosis and legal liabilities. In this work, we propose a new denoising method for medical images that not only efficiently removes various types of noise, but also preserves key medical features throughout the process. To achieve this goal, we utilize a gradient-based eXplainable Artificial Intelligence (XAI) approach to design a feature preserving loss function. Our feature preserving loss function is motivated by the characteristic that gradient-based XAI is sensitive to noise. Through backpropagation, medical image features before and after denoising can be kept consistent. We conducted extensive experiments on three available medical image datasets, including synthesized 13 different types of noise and artifacts. The experimental results demonstrate the superiority of our method in terms of denoising performance, model explainability, and generalization.
The Missing U for Efficient Diffusion Models
Authors: Sergio Calvo-Ordonez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Date: 2023-10-31 00:00:00
Tasks: Denoising, Image Generation, Video Generation

Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse process, remains a challenge due to slow convergence rates and high computational costs. In this paper, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models that is more parameter-efficient, exhibits faster convergence, and demonstrates increased noise robustness. Experimenting with Denoising Diffusion Probabilistic Models (DDPMs), our framework operates with approximately a quarter of the parameters, and $\sim$ 30\% of the Floating Point Operations (FLOPs) compared to standard U-Nets in DDPMs. Furthermore, our model is notably faster in inference than the baseline when measured in fair and equal conditions. We also provide a mathematical intuition as to why our proposed reverse process is faster as well as a mathematical discussion of the empirical tradeoffs in the denoising downstream task. Finally, we argue that our method is compatible with existing performance enhancement techniques, enabling further improvements in efficiency, quality, and speed.
Facial asymmetry: A Computer Vision based behaviometric index for assessment during a face-to-face interview
Authors: Shuvam Keshari, Tanusree Dutta, Raju Mullick, Ashish Rathor, Priyadarshi Patnaik
Date: 2023-10-30 00:00:00

Choosing the right person for the right job makes the personnel interview process a cognitively demanding task. Psychometric tests, followed by an interview, have often been used to aid the process although such mechanisms have their limitations. While psychometric tests suffer from faking or social desirability of responses, the interview process depends on the way the responses are analyzed by the interviewers. We propose the use of behaviometry as an assistive tool to facilitate an objective assessment of the interviewee without increasing the cognitive load of the interviewer. Behaviometry is a relatively little explored field of study in the selection process, that utilizes inimitable behavioral characteristics like facial expressions, vocalization patterns, pupillary reactions, proximal behavior, body language, etc. The method analyzes thin slices of behavior and provides unbiased information about the interviewee. The current study proposes the methodology behind this tool to capture facial expressions, in terms of facial asymmetry and micro-expressions. Hemi-facial composites using a structural similarity index was used to develop a progressive time graph of facial asymmetry, as a test case. A frame-by-frame analysis was performed on three YouTube video samples, where Structural similarity index (SSID) scores of 75% and more showed behavioral congruence. The research utilizes open-source computer vision algorithms and libraries (python-opencv and dlib) to formulate the procedure for analysis of the facial asymmetry.
LinFlo-Net: A two-stage deep learning method to generate simulation ready meshes of the heart
Authors: Arjun Narayanan, Fanwei Kong, Shawn Shadden
Date: 2023-10-30 00:00:00
Tasks: Deep Learning

We present a deep learning model to automatically generate computer models of the human heart from patient imaging data with an emphasis on its capability to generate thin-walled cardiac structures. Our method works by deforming a template mesh to fit the cardiac structures to the given image. Compared with prior deep learning methods that adopted this approach, our framework is designed to minimize mesh self-penetration, which typically arises when deforming surface meshes separated by small distances. We achieve this by using a two-stage diffeomorphic deformation process along with a novel loss function derived from the kinematics of motion that penalizes surface contact and interpenetration. Our model demonstrates comparable accuracy with state-of-the-art methods while additionally producing meshes free of self-intersections. The resultant meshes are readily usable in physics based simulation, minimizing the need for post-processing and cleanup.
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal
Authors: Kevin Zhang, Sakshum Kulshrestha, Christopher Metzler
Date: 2023-10-30 00:00:00
Tasks: Active Learning, Denoising

Despite recent advances, developing general-purpose universal denoising and artifact-removal networks remains largely an open problem: Given fixed network weights, one inherently trades-off specialization at one task (e.g.,~removing Poisson noise) for performance at another (e.g.,~removing speckle noise). In addition, training such a network is challenging due to the curse of dimensionality: As one increases the dimensions of the specification-space (i.e.,~the number of parameters needed to describe the noise distribution) the number of unique specifications one needs to train for grows exponentially. Uniformly sampling this space will result in a network that does well at very challenging problem specifications but poorly at easy problem specifications, where even large errors will have a small effect on the overall mean squared error. In this work we propose training denoising networks using an adaptive-sampling/active-learning strategy. Our work improves upon a recently proposed universal denoiser training strategy by extending these results to higher dimensions and by incorporating a polynomial approximation of the true specification-loss landscape. This approximation allows us to reduce training times by almost two orders of magnitude. We test our method on simulated joint Poisson-Gaussian-Speckle noise and demonstrate that with our proposed training strategy, a single blind, generalist denoiser network can achieve peak signal-to-noise ratios within a uniform bound of specialized denoiser networks across a large range of operating conditions. We also capture a small dataset of images with varying amounts of joint Poisson-Gaussian-Speckle noise and demonstrate that a universal denoiser trained using our adaptive-sampling strategy outperforms uniformly trained baselines.