The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching
The central goal of the deep-learning-based microscopic image SR tasks is to reconstruct the high-frequency structures with high accuracy from LR images. Therefore, in pursuit of high fidelity, the mostly used loss functions in microscopic image restoration are mean absolute error (MAE) loss, mea...
Understand the latest techniques, models, and applications of image super-resolution in deep learning and computer vision. A comprehensive guide for research…
The central goal of the deep-learning-based microscopic image SR tasks is to reconstruct the high-frequency structures with high accuracy from LR images. Therefore, in pursuit of high fidelity, the mostly used loss functions in microscopic image restoration are mean absolute error (MAE) loss, mea...
Overall, super-resolution is a pretty cool application of deep learning. It’s now possible to build very cool image enhancer software with deep learning to automatically apply super-resolution to images. It goes without say, as is the case with many deep learning models, it’s highly effectiv...
The reconstructed HR image obtained by the sparse reconstruction stage is used as the input of the MR network structure to optimize the high-frequency detail residual information. Finally, we can obtain a higher quality super-resolution image compared with the SCSR algorithm and the VDSR algorithm....
(CNN) or generative adversarial networks (GAN) to predict high-frequency details lost in low- resolution images, we provide a general overview on background technologies and pay special attention to super-resolution methods built on deep learning architectures for real-time super-resolution, which ...
of super-resolution, a residual image is the difference between a high-resolution reference image and a low-resolution image that has been upscaled using bicubic interpolation to match the size of the reference image. A residual image contains information about the high-frequency details of an ...
Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volum...
The trained convolutional network yielded sharp super-resolution images in which most of the high-frequency components were recovered. In conclusion, we showed that a deep learning-based approach has great potential when it comes to increasing the resolution of low-field MR images....