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.
As in various fields like scientific research and industrial application, the computation time optimization is becoming a task that is of increasing importance because of its highly parallel architecture. The graphics processing unit is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Based on this, an algorithm was introduced in this paper to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image, and strategies (e.g., "copying", "image partitioning", etc.) were proposed to optimize the parallel algorithm. Results indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU by the use of CUDA was 50 times faster than that of the GLCM computation by CPU, which manifested significantly improved performance.
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