Abstract:Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise. Below, we extend our preliminary paper [59] by several mechanisms to address the above issues: I) We present a novel self… Show more
“…Deep learning has made significant progress in solving image deblurring, and many deep learning networks have been proposed for this purpose. [17][18][19][20] Ramakrishnan et al 21 proposed a deep filtering method based on generative adversarial network (GAN) and dense architecture. Their paper provides innovative ideas for removing motion blur.…”
The deblurring of flotation froth images significantly aids in the characterization of coal flotation and fault diagnosis. Images of froth acquired at a flotation site contain considerable noise and blurring, making feature extraction and segmentation processing difficult. We present an effective method for deblurring froth images based on disentangled representations. Disentangled representation is achieved by separating the content and blur features in the blurred image using a content encoder and a blur encoder. Then, the separated feature vectors are embedded into a deblurring framework to deblur the froth image. The experimental results show that this method achieves a superior deblurring effect on froth images under various conditions, which lays the foundation for the intelligent adjustment of parameters to guide the flotation site.
“…Deep learning has made significant progress in solving image deblurring, and many deep learning networks have been proposed for this purpose. [17][18][19][20] Ramakrishnan et al 21 proposed a deep filtering method based on generative adversarial network (GAN) and dense architecture. Their paper provides innovative ideas for removing motion blur.…”
The deblurring of flotation froth images significantly aids in the characterization of coal flotation and fault diagnosis. Images of froth acquired at a flotation site contain considerable noise and blurring, making feature extraction and segmentation processing difficult. We present an effective method for deblurring froth images based on disentangled representations. Disentangled representation is achieved by separating the content and blur features in the blurred image using a content encoder and a blur encoder. Then, the separated feature vectors are embedded into a deblurring framework to deblur the froth image. The experimental results show that this method achieves a superior deblurring effect on froth images under various conditions, which lays the foundation for the intelligent adjustment of parameters to guide the flotation site.
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