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Motion deblurring via multiscale residual convolutional dictionary learning
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AI
DOI：10.1016/j.dsp.2025.105337.png)
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Abstract
En 中文
Sparsity-based methods have been extensively used for restoration of blurry images. To overcome the limitation found in patch-based sparse representation models that the shift-invariant properties of images are often ignored, convolutional sparse coding (CSC) models have been developed. However, existing CSC models often perform less effectively than leading convolutional neural network (CNN) or Transformer models on the image restoration task. We hypothesize that the reasons are two-fold: One reason is that most existing CSC models often represent images via a single scale by using a universal dictionary, while many CNN models extract multiscale features via different convolutional kernels. The second reason is that existing CSC-based models focus on reconstructing images, whereas CNN/Transformer-based networks focus on reconstructing residual features. These findings inspired us to develop a multiscale residual CSC network. Specifically, we employ a U-shape Transformer network for multiscale feature extraction, and we solve the joint residual-CSC optimization problem by using the efficient learned iterative shrinkage/thresholding algorithm. Experimental results tested on various motion-blur image datasets demonstrate the effectiveness of our model as compared with other state-of-the-art deblurring methods.
Keywords:
Motion deblurring
Convolutional sparse coding
Residual learning
Learned iterative shrinkage/thresholding
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