Learning Deep Gradient Descent Optimization for Image Deconvolution

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2020-12, Vol.31 (12), p.5468-5482
Hauptverfasser:	Gong, Dong, Zhang, Zhen, Shi, Qinfeng, van den Hengel, Anton, Shen, Chunhua, Zhang, Yanning
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Benchmarks Blurring Deconvolution Deep gradient descent image deblurring image deconvolution Image restoration Inverse problems Kernel Learning learning to optimize Machine learning Neural networks Noise level Noise levels Optimization Regularization Static models
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