Morgan State University

Greig Richmond

Arlene Cole-Rhodes

A scale-based, convolutional, long-short term memory (LSTM) network is developed for image deblurring. Multi-scale information is obtained using dilated convolutions shared between scales using recurrent connections resulting in low-parameter count and to deblur an image without the use of prior information. Effectiveness is evaluated with industry standard datasets. Results show that a comparable sharp image can be recovered more efficiently even with a significant reduction in the total number of network parameters.

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Severn, MD, United States of America

Greig Richmond