A system and method to use deep learning for super resolution in a radar system include obtaining first-resolution time samples from reflections based on transmissions by a first-resolution radar system of multiple frequency-modulated signals. The first-resolution radar system includes multiple transmit...
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...
A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing ...
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...
High performance imaging in parallel cameras is a worldwide challenge in computational optics studies. However, the existing solutions are suffering from a fundamental contradiction between the field of view (FOV), resolution and bandwidth, in which syst
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...
1.1 Supervised learning Figure 1.1 Machine learning is an area of artificial intelligence that fits mathematical models to observed data. It can coarsely be divided into supervised learning, unsupervised learning, and reinforcement learning. Deep neural networks contribute to each of these areas. ...
2.3. Super-Resolution model based on hierarchical feature combination The low-resolution images lost a lot of high-frequency information compared to the high-resolution images, and every pixel should be repaired with the information from its surroundings. So when reconstructing high-resolution images, ...
Create a low-resolution version of the high-resolution reference image by usingimresizewith a scaling factor of 0.25. The high-frequency components of the image are lost during the downscaling. Get scaleFactor = 0.25; Ilowres = imresize(Ireference,scaleFactor,"bicubic"); ...
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....