Low-resolution medical images can hamper medical diagnosis seriously, especially in the analysis of retina images and specifically for the detection of macula fovea. Therefore, improving the quality of medical images and speeding up their reconstruction is particularly important for expert diagnosis. To deal with this engineering problem, our paper presents a fast medical image super-resolution (FMISR) method whereby the three hidden layers to complete feature extraction is as same as the super resolution convolution neural network. It is important that a well-designed deep learning network processes images in the low resolution instead of the high-resolution space and enables the super-resolution reconstruction to be more efficient. Sub-pixel convolution layer addition and mini-network substitution in the hidden layers are critical for improving the image reconstruction speed. While the hidden layers are proposed for ensuring reconstruction quality, our FMISR framework performs significantly faster and produces a higher resolution images. As such, the technique underlying this framework presents a high potential in retinal macular examination as it provides a good platform for the segmentation of retinal images. INDEX TERMS Super resolution, medical imaging, deep learning, medical diagnosis.
Currently, unmanned aerial vehicles (UAV) are applied to routine inspection tasks of power transmission devices. Deep-learning algorithm and machine vision have attracted much attention in the field of the UAV’s autonomous control as it’s an effective way to improve the efficiency of inspection. Considering the differences between the distant and close view, this paper adopts Mask R-CNN to detect various components of power transmission devices but use the methods such as processing of the edge, hole filling and Hough Transform identify the wires in distant. Some major components, such as pole, truss, cross arm, insulator string and so on, can be 100% recognized. This proposed model shows the characteristics of high recognition speed and high accuracy, which can assist UAV to inspect well.
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