21 datasets found

A dataset for wireless mobile channel modeling, including ray tracing and deep learning fusion super-resolution modeling method for cluster characteristics prediction.

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel...

A novel deep-learning approach based on generative adversarial networks to perform super-resolution reconstruction of sparse measurements.

Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception.

A real microscope image was used to test the method. The image was deconvolved using the proposed method, and the results were compared with the original image.

A synthetic image was synthesized to test the method. The image was convolved with the IRF, added noise and background, and then deconvolved using the proposed method.

The dataset used for training the Pixel2Style2Pixel encoder consists of degraded copies of high-resolution images.

The dataset used for super-resolution is DIV2K, CelebA, and Set5.

High-resolution diffusion-weighted images (DWIs) from low-resolution DWIs using a deep learning-based super-resolution method entitled “SRDTI” to synthesize high-resolution...

RT-urban dataset, a high-precision channel feature dataset for training a deep learning model for super-resolution of channel characteristics.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ImageNet and several famous super-resolution image datasets.

The dataset used for testing the self-similarity-based super-resolution model for photoacoustic angiography.

The dataset used for training the self-similarity-based super-resolution model for photoacoustic angiography.

The dataset used for training the self-similarity-based super-resolution model for photoacoustic angiography.

MRI images super-resolution reconstruction using deep learning techniques

MRI images super-resolution reconstruction using deep learning techniques

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.

The dataset used for training and testing the proposed Incremental Residual Learning (IRL) framework.

Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. The dataset used in this...

The dataset used in the paper is a synthetic dataset for blind super-resolution, consisting of low-resolution images and their corresponding blur kernels.