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Modified watershed approach for segmentation of complex optical coherence tomographic images
Watershed segmentation method has been used in various applications.
Frequency-domain Blind Quality Assessment of Blurred and Blocking-artefact Images using Gaussian Process Regression model
Also, in bokeh mode images they are commonly seen: blurriness as a result of intentional blurred background while blocking artifact and global blurriness arising due to compression.
Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein
Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0. 9932.
Opto-UNet: Optimized UNet for Segmentation of Varicose Veins in Optical Coherence Tomography
Furthermore, the depth wise separable convolution significantly reduces the complexity of the network by optimizing the number of parameters.
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