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Unsupervised Despeckling
TLDR
In this article, an unsupervised deep adversarial approach was used to address the despeckling problem using an adversarial loss imposed by a discriminator to differentiate between the deseckled images generated by the DRNN and the set of high-quality images.Abstract:
Contrast and quality of ultrasound images are adversely affected by the excessive presence of speckle However, being an inherent imaging property, speckle helps in tissue characterization and tracking Thus, despeckling of the ultrasound images requires the reduction of speckle extent without any oversmoothing In this letter, we aim to address the despeckling problem using an unsupervised deep adversarial approach A despeckling residual neural network (DRNN) is trained with an adversarial loss imposed by a discriminator The discriminator tries to differentiate between the despeckled images generated by the DRNN and the set of high-quality images Further to prevent the developed DRNN from oversmoothing, a structural loss term is used along with the adversarial loss Experimental evaluations show that the proposed DRNN is able to outperform the state-of-the-art despeckling approachesread more
Citations
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Proceedings ArticleDOI
Despeckling CNN with Ensembles of Classical Outputs
TL;DR: A convolutional neural network is developed which learns to remove speckle from US images using the outputs of these classical approaches and is able to outperform the state-of-the-art despeckling approaches and produces the outputs even better than the ensembles for certain images.
References
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