CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Guo, Yi; Gao, Yuanhang; Hu, Bingliang; Qian, Xueming; Liang, Dong

doi:10.3390/s24010042

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…It accomplishes this by modeling global context through self-attention mechanisms across channels, thereby reducing computational overhead. More recently, Guo [20] proposed a pixel-wise crossmodal image denoising method (CMID) based on deep reinforcement learning, aiming to effectively address noise across different modalities. However, the performance of networks trained on synthetic data may significantly degrade when tested on real-world data due to domain gaps between synthetic and real datasets.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

Geng,

Xu,

Xiong

et al. 2024

Applied Sciences

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While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications.

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Section: Introductionmentioning

confidence: 99%

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

Geng,

Xu,

Xiong

et al. 2024

Applied Sciences

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Real‐World Image Deraining Using Model‐Free Unsupervised Learning

Yu,

Xiang,

Shu

et al. 2024

International Journal of Intelligent Systems

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We propose a novel model‐free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real‐world image deraining, dubbed MUL‐Derain. Beyond existing unsupervised deraining efforts, MUL‐Derain leverages a model‐free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL‐Derain can efficiently compute spatial coherence and global interactions by modeling long‐range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL‐Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real‐world datasets demonstrate that our MUL‐Derain obtains state‐of‐the‐art performance over un/semisupervised methods and exhibits competitive advantages over the fully‐supervised ones.

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CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Cited by 2 publications

References 41 publications

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

Real‐World Image Deraining Using Model‐Free Unsupervised Learning

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