ShaDocFormer: A Transformer-Based Approach for Document Shadow Removal

Centrala begrepp

ShaDocFormer is a Transformer-based architecture that effectively removes shadows from document images by integrating traditional methodologies with deep learning techniques.

Sammanfattning

Introduction to Document Shadows:

Shadows in document images impact readability.

Challenges in Current Methods:

Inaccurate shadow mask detection and illumination estimation.

Proposed Solution - ShaDocFormer:

Integrates STD and CFR modules for precise shadow removal.

Key Contributions:

Superior performance over state-of-the-art methods.

Experimental Evaluation:

Utilized RDD and Kligler datasets for comprehensive analysis.

Methodology Overview:

Detailed explanation of STD and CFR modules.

Objective Function Formulation:

Combination of MSE, SSIM, and perceptual loss.

Datasets and Metrics:

Utilized two datasets with standardized image resolution.

Comparisons with State-of-the-Arts:

Outperformed existing methods in various benchmarks.

Ablation Studies:

Importance of each component in the success of ShaDocFormer.

Statistik

"Extensive experiments demonstrate that ShaDocFormer outperforms current state-of-the-art methods."
"The dataset RDD contains a total of 4,916 matched pairs."
"The Mean Square Error (MSE) Loss is defined as..."

Citat

"No EBO, their performance is much inferior."
"The performance of the full model is closest to the target."

Viktiga insikter från

ShaDocFormer

by Weiwen Chen,... på arxiv.org 03-21-2024

https://arxiv.org/pdf/2309.06670.pdf

Djupare frågor

How can ShaDocFormer be adapted for real-time applications?

ShaDocFormer can be adapted for real-time applications by optimizing its architecture and algorithms for efficiency. This optimization may involve reducing computational complexity, implementing parallel processing techniques, and leveraging hardware acceleration such as GPUs or TPUs to speed up inference times. Additionally, the model can be fine-tuned through transfer learning on specific datasets related to real-time document shadow removal tasks. By streamlining the processes within ShaDocFormer and enhancing its scalability, it can be tailored to meet the demands of real-time applications.

What are the potential limitations or biases in using standardized image resolutions?

Using standardized image resolutions may introduce limitations in terms of flexibility and adaptability across different devices or scenarios. If a fixed resolution is mandated, there could be challenges in handling images with varying aspect ratios or quality levels efficiently. Biases may arise if certain details crucial for accurate analysis are lost during resizing to fit into a standard resolution format. Moreover, enforcing a strict resolution requirement might restrict the model's ability to generalize well across diverse input data sources.

How can the integration of traditional algorithms with deep learning techniques benefit other computer vision tasks?

The integration of traditional algorithms with deep learning techniques offers several benefits for various computer vision tasks:

Interpretability: Traditional algorithms often provide transparent decision-making processes that help explain how models arrive at their outputs.
Robustness: Combining classical methods with deep learning approaches can enhance model robustness by leveraging the strengths of both paradigms.
Data Efficiency: Traditional algorithms may require less labeled data compared to deep learning models, making them valuable in scenarios where annotated datasets are limited.
Complementary Capabilities: Deep learning excels at feature extraction from raw data while traditional methods offer precise rule-based operations; integrating these capabilities enhances overall performance.
Resource Optimization: Hybrid models combining both types of algorithms can optimize resource utilization by utilizing simpler yet effective strategies alongside complex neural networks.

By merging traditional algorithmic principles with modern deep learning methodologies, synergistic effects emerge that address challenges more comprehensively than either approach alone could achieve in many computer vision tasks.