67 accepted papers
Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods t
Pansharpening aims to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) image to generate a highresolution multispectral output that preserves both fine spatial details and faithful spectral responses. However, enhancing spatial textures without introducing spe
Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumination in images and the inherent noise sensitivity of event signals in real-world scenarios. To address these issues, we
Gait recognition enables non-intrusive, privacy-preserving identification but suffers in uncontrolled environments due to illumination and motion sensitivity in conventional cameras. In this work, we explore gait recognition using event cameras, which offer microsecond temporal resolution and high d
Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) in image generation, recent work has adapted reward-based learning to image super-resolution (ISR) by using Image Quality Assessment (IQA) models as rewards. However, existing IQA models typically output only a single globa
Spiking Neural Networks (SNNs) draw inspiration from biological neurons to enable brain-like computation, demonstrating effectiveness in processing temporal information with energy efficiency and biological realism. Most existing SNNs are based on neural dynamics such as the (leaky) integrate-and-fi…
Diffusion models show significant potential for low-light image enhancement. However, this task requires satisfying human perceptual preferences and content fidelity transcending simple brightness and color improvement. Existing methods rely on heuristic physical priors or incorporate perceptual met
Motion blur is a common degradation in dynamic imaging. Recent studies have moved beyond restoring a single sharp image from a blurred input and instead target blur decomposition: recovering a temporally continuous sharp video sequence from one motion-blurred image. Event cameras, with their microse
The rise in sophisticated image forgery techniques, driven by advancements in image editing and generation, has posed new security challenges. Traditional methods, designed for specific tampering artifacts, struggle with out-of-distribution image forgery detection. In this paper, we propose a shift…
Rainy images suffer from quality degradation due to the synergistic effect of rain streaks and accumulation. The rain streaks are anisotropic and show a specific directional arrangement, while the rain accumulation is isotropic and shows a consistent concentration distribution in local regions. This…
All-in-one models for adverse weather removal aim to process various degraded images using a single set of parameters, making them ideal for real-world scenarios. However, they encounter two main challenges: catastrophic forgetting and limited degradation awareness. The former causes the model to lo…
Despite the advanced long-sequence modeling of Mamba, which has expanded its applications in image restoration, there remains a lack of exploration combining its strengths with the specific characteristics of JPEG image restoration, where high-frequency components are lost after the Discrete Cosine…
Ultra-high-definition (UHD) image restoration often faces computational bottlenecks and information loss due to its extremely high resolution. Existing studies based on Variational Autoencoders (VAE) improve efficiency by transferring the image restoration process from pixel space to latent space. H…
Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (e.g., 4K and 8K), achieving a balance between restoration quality and computational efficiency has become increasingly critical. Existing methods, primarily based on C
Existing ultra-high-definition (UHD) image restoration methods often struggle with consistency due to downsampling. We aim to address these challenges by leveraging the powerful latent space representation and reconstruction capabilities of Variational Autoencoders (VAE). However, applying VAE to UH…
Existing event-based video deblurring methods face limitations in extracting and fusing long-range spatiotemporal motion information from events, primarily due to restricted receptive fields or low computational efficiency, resulting in suboptimal deblurring performance.To address these issues, we i…
Pansharpening aims to generate high-resolution multispectral (MS) images by fusing panchromatic (PAN) images with corresponding low-resolution MS images. However, many existing methods struggle to fully capture spatial and spectral interactions, limiting their effectiveness. To address this, we prop…
Leveraging its robust linear global modeling capability, Mamba has notably excelled in computer vision. Despite its success, existing Mamba-based vision models have overlooked the nuances of event-driven tasks, especially in video reconstruction. Event-based video reconstruction (EBVR) demands spati…
Image deraining aims to remove rain streaks from rainy images and restore clear backgrounds. Currently, some research that employs the Fourier transform has proved to be effective for image deraining, due to it acting as an effective frequency prior for capturing rain streaks. However, despite there…
Ultra-High Definition (UHD) image restoration struggles to balance computational efficiency and detail retention. While Variational Autoencoders (VAEs) offer improved efficiency by operating in the latent space, with the Gaussian variational constraint, this compression preserves semantics but sacri…
Concern for image authenticity spurs research in image forgery detection and localization (IFDL). Most deep learning-based methods focus primarily on spatial domain modeling and have not fully explored frequency domain strategies. In this paper, we observe and analyze the frequency characteristic ch
Event cameras, which capture pixel-level brightness changes asynchronously, provide rich motion information that is often missed during traditional frame-based camera exposures, thereby offering fresh perspectives for motion deblurring. Although current approaches incorporate event intensity, they n…
The integration of differential equations with neural networks has created powerful tools for modeling complex dynamics effectively across diverse machine learning applications. While standard integer-order neural ordinary differential equations (ODEs) have shown considerable success, they are limit…
Continual Test-Time Adaptation (CTTA) aims to online adapt a pre-trained model to changing environments during inference. Most existing methods focus on exploiting target data, while overlooking another crucial source of information, the pre-trained weights, which encode underutilized domain-invaria…
Pansharpening combines panchromatic and low-resolution multispectral images to generate high-resolution multispectral images. Previous studies have explored the connection between pansharpening and the frequency domain, but mostly in the real-valued domain, leaving the complex domain relatively unex
The iterative sampling procedure employed by diffusion models (DMs) often leads to significant latency. To address this, we propose Stochastic Consistency Distillation (SCott) to enable accelerated text-to-image generation, where high-quality generations can be achieved with just 2-4 sampling steps…
We introduce UHD-Processor, a unified and robust framework for all-in-one image restoration, which is particularly resource-efficient for Ultra-High-Definition (UHD) images. To address the limitations of traditional all-in-one methods that rely on complex restoration backbones, our strategy employs…
Consistency Models (CMs) have showed a promise in creating high-quality images with few steps. However, the way to add new conditional controls to the pre-trained CMs has not been explored. In this paper, we explore the pivotal subject of leveraging the generative capacity and efficiency of consiste…
Image restoration poses a garners substantial interest due to the exponential surge in demands for recovering high-quality images from diverse mobile camera devices, adverse lighting conditions, suboptimal shooting environments, and frequent image compression for efficient transmission purposes. Yet…
The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise. To tackle with this issue we present a novel strategy from the view of shadow transformation in this paper: directly homoge…
This study introduces a new method for detecting and localizing image forgery by focusing on manipulation traces within the noise domain. We posit that nearly invisible noise in RGB images carries tampering traces, useful for distinguishing and locating forgeries. However, the advancement of tamperi…
"Traditional image deblurring struggles with high-quality reconstruction due to limited motion data from single blurred images. Excitingly, the high-temporal resolution of event cameras records motion more precisely in a different modality, transforming image deblurring. However, many event camera-b…
Convolutional neural networks-based video de-raining methods commonly rely on dense intensity frames captured by CMOS sensors. However, the limited temporal resolution of these sensors hinders the capture of dynamic rainfall information, limiting further improvement in de-raining performance. This s…
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly ad…
In recent years, deep convolutional neural networks (CNNs) have become dominant in MRI reconstruction from undersampled k-space. However, most existing CNNs methods reconstruct the undersampled images either in the spatial domain or in the frequency domain, and neglecting the correlation between the…
Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradations, e…
Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using the single set of network parameters. In this paper, we find that distorted images under different weather conditions contain general characteristics as well as their specific characteristic…
Non-local interactions play a vital role in boosting performance for image restoration. However, local window Transformer has been preferred due to its efficiency for processing high-resolution images. The superiority in efficiency comes at the cost of sacrificing the ability to model non-local inte…
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxilia…
Convolutional image deraining networks have achieved great success while suffering from tremendous computational and memory costs. Most model compression methods require original data for iterative fine-tuning, which is limited in real-world applications due to storage, privacy, and transmission con…
Deep Convolutional Neural Networks (CNNs) based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially variant prope…
Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial b…
Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To add…
Images captured with improper exposures usually bring unsatisfactory visual effects. Previous works mainly focus on either underexposure or overexposure correction, resulting in poor generalization to various exposures. An alternative solution is to mix the multiple exposure data for training a sing…
"To meet the needs of practical applications, current deep learning-based methods focus on using a single model to handle JPEG images with different compression qualities, while few of them consider the auxiliary effects of the compression quality information. Recently, several methods estimate qual…
Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into diffe…
Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success. However, most existing methods are designed in a black-box principle, lacking sufficient interpretability. Additionally, they ignore th…
Pan-sharpening aims to integrate the complementary information of texture-rich PAN images and multi-spectral (MS) images to produce the texture-rich MS images. Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information lea…
In remote sensing imaging systems, pan-sharpening is an important technique to obtain high-resolution multispectral images from a high-resolution panchromatic image and its corresponding low-resolution multispectral image. Owing to the powerful learning capability of convolution neural network (CNN)…
"Pan-sharpening aims to generate the high-resolution multi-spectral (MS) images by fusing PAN images and low-resolution MS images. Despite the great advances, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solution in frequency domain. In this p…
Thanks to the powerful representation capabilities, transformers have made impressive progress in image restoration. However, existing transformers-based methods do not carefully consider the particularities of image restoration. In general, image restoration requires that an ideal approach should b…
In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e.g., cross-resolution and insufficient lighting. Due to the limited variation range of training data, existing models are difficult to generaliz…
Recently, deep learning-based image denoising methods have achieved significant improvements over traditional methods. Due to the hardware limitation, most deep learning-based image denoising methods utilize cropped small patches to train a convolutional neural network to infer the clean images. How…
While deep convolutional neural networks (CNNs) have achieved great success on image de-raining task, most existing methods can only learn fixed mapping rules between paired rainy/clean images on a single dataset. This limits their applications in practical situations with multiple and incremental d…
Most existing image de-raining networks could only learn fixed mapping rules between paired rainy/clean images on single synthetic dataset and then stay static for lifetime. However, since single synthetic dataset merely provides a partial view for the distribution of rain streaks, the deep models w…
Deep learning (DL)-based methods have achieved great success in solving the ill-posed JPEG compression artifacts removal problem. However, as most DL architectures are designed to directly learn pixel-level mapping relationships, they largely ignore semantic-level information and lack sufficient int…
Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is…
Compact video super-resolution (VSR) networks can be easily deployed on resource-limited devices, e.g., smart-phones and wearable devices, but have considerable performance gaps compared with complicated VSR networks that require a large amount of computing resources. In this paper, we aim to improv…
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping ne…
Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly…
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On the one hand, these degradations lead to severe discriminative information loss, which significantly obstructs identity…
Pan-sharpening aims at fusing spectral and spatial information, which are respectively contained in the multispectral (MS) image and panchromatic (PAN) image, to produce a high resolution multi-spectral (HRMS) image. In this paper, a new variational model based on a local gradient constraint for pan…
To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the fea…
With the development of underwater optical sensors, manmade object recognition from underwater optical images has attracted wide attention. Deep learning methods have demonstrated impressive performance in object recognition tasks from natural images. However, it is difficult to collect large-scale…
We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled m…
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to dire…
We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transform…