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SpA-Former: Transformer image shadow detection and removal via spatial attention (2206.10910v3)

Published 22 Jun 2022 in cs.CV and cs.LG

Abstract: In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpA-former is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/zhangbaijin/SpA-Former-shadow-removal

Citations (23)

Summary

  • The paper introduces an end-to-end transformer framework that unifies shadow detection and removal in a single operation.
  • It employs a two-wheel RNN with spatial attention and a joint Fourier transform residual block to efficiently capture both fine details and overall image structure.
  • Evaluation on the ISTD dataset shows improved RMSE, SSIM, and PSNR, highlighting its advanced performance and resource efficiency.

Overview of SpA-Former: Transformer Image Shadow Detection and Removal via Spatial Attention

The research paper presents SpA-Former, an innovative framework for simultaneous shadow detection and removal from images, effectively addressing prevalent challenges in the domain of image processing. This work circumvents the conventional two-step approaches by introducing an end-to-end one-stage pipeline, which unifies shadow detection and removal into a singular operation, leveraging the advanced capabilities of transformer networks infused with spatial attention mechanisms.

Methodological Contributions

SpA-Former integrates several computational strategies to achieve its objectives:

  1. Transformer-Based Architecture: The paper introduces a transformer network to model both short and long-range dependencies within images, facilitating accurate texture and structural feature extraction necessary for shadow removal. The transformer module computes attention across channels rather than spatial dimensions. This technique mitigates the computational overhead traditionally associated with attention mechanisms, offering linear complexity and enabling the network to maintain fine-grained details across diverse shadow detection and removal tasks.
  2. Two-Wheel RNN Joint Spatial Attention: This component enhances the model's ability to focus on crucial areas within the image. It employs a dual-directional RNN framework capable of discerning spatial context, similar to cloud-impacted regions. This, combined with spatial attention, enables precise detection and removal of shadows across various image segments, thereby improving the model’s performance on images with complex shadow patterns.
  3. Joint Fourier Transform Residual Block: To effectively reconstruct shadow-free images, the framework employs a joint Fourier transform residual block that captures both high and low-frequency image components, ensuring both detailed and consistent shadow removal. This mechanism is adept at managing the intricate balance between preserving image detail and achieving comprehensive shadow removal.
  4. Multi-Faceted Loss Function: The loss function combines elements of adversarial loss with pixel-wise and attention-based metrics to optimize both the accuracy and realism of shadow-free images.

Evaluation and Results

The experimental validation was conducted on the ISTD dataset, showcasing SpA-Former’s superior capabilities in shadow removal compared to existing methodologies. Across metrics such as RMSE, SSIM, and PSNR, SpA-Former demonstrates improved performance in various scenarios involving shadow and non-shadow regions.

Implications and Future Directions

The introduction of SpA-Former marks a significant advancement in the image processing field, particularly for tasks requiring shadow manipulation. Practically, its one-stage architecture simplifies deployment, potentially demanding fewer computational resources compared to traditional networks, thus lowering barriers for widespread application and integration into real-time systems.

Theoretically, SpA-Former’s integration of transformers into the image processing pipeline serves as a cornerstone for future research in AI-driven image manipulation tasks. Future developments could explore the extension of this framework to accommodate other image restoration tasks, such as rain or snow removal, expanding its applicability. Additionally, further optimization could involve enhancing transformer efficiency or developing more sophisticated attention mechanisms tailored for specific image restoration challenges.

By consolidating a high degree of accuracy and resource efficiency, SpA-Former represents a noteworthy contribution to the field of image shadow processing, inviting further exploration and refinement in future endeavors.