Leveraging Contrast Information for Efficient Document Shadow Removal

Document shadows are a major obstacle in the digitization process. Due to the dense information in text and patterns covered by shadows, document shadow removal requires specialized methods. Existing document shadow removal methods, although showing some progress, still rely on additional information such as shadow masks or lack generalization and effectiveness across different shadow scenarios. This often results in incomplete shadow removal or loss of original document content and tones. Moreover, these methods tend to underutilize the information present in the original shadowed document image. In this paper, we refocus our approach on the document images themselves, which inherently contain rich information.We propose an end-to-end document shadow removal method guided by contrast representation, following a coarse-to-fine refinement approach. By extracting document contrast information, we can effectively and quickly locate shadow shapes and positions without the need for additional masks. This information is then integrated into the refined shadow removal process, providing better guidance for network-based removal and feature fusion. Extensive qualitative and quantitative experiments show that our method achieves state-of-the-art performance.

LBM: Latent Bridge Matching for Fast Image-to-Image Translation

In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. We show that the method can reach state-of-the-art results for various image-to-image tasks using only a single inference step. In addition to its efficiency, we also demonstrate the versatility of the method across different image translation tasks such as object removal, normal and depth estimation, and object relighting. We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation. We provide an open-source implementation of the method at https://github.com/gojasper/LBM.

Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling

To meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves capturing CO2 and injecting it into deep geological formations for long-term storage, requiring precise monitoring to ensure containment and prevent leakage. Time-lapse seismic imaging is essential for tracking CO2 migration but often struggles to capture the complexities of multi-phase subsurface flow. Digital Shadows (DS), leveraging machine learning-driven data assimilation techniques such as nonlinear Bayesian filtering and generative AI, provide a more detailed, uncertainty-aware monitoring approach. By incorporating uncertainties in reservoir properties, DS frameworks improve CO2 migration forecasts, reducing risks in GCS operations. However, data assimilation depends on assumptions regarding reservoir properties, rock physics models, and initial conditions, which, if inaccurate, can compromise prediction reliability. This study demonstrates that augmenting forecast ensembles with diverse rock physics models mitigates the impact of incorrect assumptions and improves predictive accuracy, particularly in differentiating uniform versus patchy saturation models.

Prompt-Aware Controllable Shadow Removal

Shadow removal aims to restore the image content in shadowed regions. While deep learning-based methods have shown promising results, they still face key challenges: 1) uncontrolled removal of all shadows, or 2) controllable removal but heavily relies on precise shadow region masks.To address these issues, we introduce a novel paradigm: prompt-aware controllable shadow removal. Unlike existing approaches, our paradigm allows for targeted shadow removal from specific subjects based on user prompts (e.g., dots, lines, or subject masks). This approach eliminates the need for shadow annotations and offers flexible, user-controlled shadow removal.Specifically, we propose an end-to-end learnable model, the \emph{\textbf{P}}rompt-\emph{\textbf{A}}ware \emph{\textbf{C}}ntrollable \emph{\textbf{S}}hadow \emph{\textbf{R}}emoval \emph{\textbf{Net}}work (PACSRNet). PACSRNet consists of two key modules: a prompt-aware module that generates shadow masks for the specified subject based on the user prompt, and a shadow removal module that uses the shadow prior from the first module to restore the content in the shadowed regions.Additionally, we enhance the shadow removal module by incorporating feature information from the prompt-aware module through a linear operation, providing prompt-guided support for shadow removal.Recognizing that existing shadow removal datasets lack diverse user prompts, we contribute a new dataset specifically designed for prompt-based controllable shadow removal.Extensive experimental results demonstrate the effectiveness and superiority of PACSRNet.

Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer

Image shadow removal is a crucial task in computer vision. In real-world scenes, shadows alter image color and brightness, posing challenges for perception and texture recognition. Traditional and deep learning methods often overlook the distinct needs for handling hard and soft shadows, thereby lacking detailed processing to specifically address each type of shadow in images.We propose a dual-path model that processes these shadows separately using specially designed loss functions to accomplish the hard and soft shadow removal. The model classifies shadow types and processes them through appropriate paths to produce shadow-free outputs, integrating a Vision Transformer with UNet++ for enhanced edge detail and feature fusion. Our model outperforms state-of-the-art methods and achieves 2.905 RMSE value on the ISTD dataset, which demonstrates greater effectiveness than typical single-path approaches.

OCORD: Open-Campus Object Removal Dataset

The rapid advancements in generative models, particularly diffusion-based techniques, have revolutionized image inpainting tasks by enabling the generation of high-fidelity and diverse content. However, object removal remains under-explored as a specific subset of inpainting, facing challenges such as inadequate semantic understanding and the unintended generation of artifacts. Existing datasets for object removal often rely on synthetic data, which fails to align with real-world scenarios, limiting model performance. Although some real-world datasets address these issues partially, they suffer from scalability, annotation inefficiencies, and limited realism in physical phenomena such as lighting and shadows. To address these limitations, this paper introduces a novel approach to object removal by constructing a high-resolution real-world dataset through long-duration video capture with fixed camera settings. Leveraging advanced tools such as Grounding-DINO, Segment-Anything-Model, and MASA for automated annotation, we provides image, background, and mask pairs while significantly reducing annotation time and labor. With our efficient annotation pipeline, we release the first fully open, high-resolution real-world dataset for object removal, and improved performance in object removal tasks through fine-tuning of pre-trained diffusion models.

Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation

Large-scale generative models have achieved remarkable advancements in various visual tasks, yet their application to shadow removal in images remains challenging. These models often generate diverse, realistic details without adequate focus on fidelity, failing to meet the crucial requirements of shadow removal, which necessitates precise preservation of image content. In contrast to prior approaches that aimed to regenerate shadow-free images from scratch, this paper utilizes diffusion models to generate and refine image residuals. This strategy fully uses the inherent detailed information within shadowed images, resulting in a more efficient and faithful reconstruction of shadow-free content. Additionally, to revent the accumulation of errors during the generation process, a crosstimestep self-enhancement training strategy is proposed. This strategy leverages the network itself to augment the training data, not only increasing the volume of data but also enabling the network to dynamically correct its generation trajectory, ensuring a more accurate and robust output. In addition, to address the loss of original details in the process of image encoding and decoding of large generative models, a content-preserved encoder-decoder structure is designed with a control mechanism and multi-scale skip connections to achieve high-fidelity shadow-free image reconstruction. Experimental results demonstrate that the proposed method can reproduce high-quality results based on a large latent diffusion prior and faithfully preserve the original contents in shadow regions.

MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis

Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis. Notably, we optimize the learning of the intermediate features from Shadow Analyzer to guide Shadow Synthesizer to generate more realistic shadows that blend seamlessly with the scene. Extensive evaluations on multiple shadow benchmark datasets show significant improvements of MetaShadow over the existing state-of-the-art methods on object-centered shadow detection, removal, and synthesis. MetaShadow excels in image-editing tasks such as object removal, relocation, and insertion, pushing the boundaries of object-centered image editing.

Generative Video Propagation

Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be addressed in a unified way by leveraging the generative power of such models. Specifically, our framework, GenProp, encodes the original video with a selective content encoder and propagates the changes made to the first frame using an image-to-video generation model. We propose a data generation scheme to cover multiple video tasks based on instance-level video segmentation datasets. Our model is trained by incorporating a mask prediction decoder head and optimizing a region-aware loss to aid the encoder to preserve the original content while the generation model propagates the modified region. This novel design opens up new possibilities: In editing scenarios, GenProp allows substantial changes to an object's shape; for insertion, the inserted objects can exhibit independent motion; for removal, GenProp effectively removes effects like shadows and reflections from the whole video; for tracking, GenProp is capable of tracking objects and their associated effects together. Experiment results demonstrate the leading performance of our model in various video tasks, and we further provide in-depth analyses of the proposed framework.

ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal

Image shadow removal is a typical low-level vision problem, where the presence of shadows leads to abrupt changes in brightness in certain regions, affecting the accuracy of upstream tasks. Current shadow removal methods still face challenges such as residual boundary artifacts, and capturing feature information at shadow boundaries is crucial for removing shadows and eliminating residual boundary artifacts. Recently, Mamba has achieved remarkable success in computer vision by globally modeling long-sequence information with linear complexity. However, when applied to image shadow removal, the original Mamba scanning method overlooks the semantic continuity of shadow boundaries as well as the continuity of semantics within the same region. Based on the unique characteristics of shadow images, this paper proposes a novel selective scanning method called boundary-region selective scanning. This method scans boundary regions, shadow regions, and non-shadow regions independently, bringing pixels of the same region type closer together in the long sequence, especially focusing on the local information at the boundaries, which is crucial for shadow removal. This method combines with global scanning and channel scanning to jointly accomplish the shadow removal. We name our model ShadowMamba, the first Mamba-based model for shadow removal. Extensive experimental results show that our method outperforms current state-of-the-art models across most metrics on multiple datasets. The code for ShadowMamba is available at (Code will be released upon acceptance).