It seems that you're asking about neural networks for single-image super-resolution. I think that your questions will be answered in a review paper on the topic, such as this one: Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue, Qingmin Liao "Deep Learning for Single Image Super-Resolution: A Brief Review".
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.
To answer your specific questions:
What type of neural network is used for this?
Variants of convolutional neural networks, which stats.SE tags as conv-neural-network. Particular approaches and networks are compared and contrasted in the article.
What type data are needed to train neural network?
According to the article, the typical procedure is to take a high-resolution image, blur/downsample the image to make a low-resolution image. The network's task is to attempt to reconstruct the high-resolution image from the low-resolution image.
What should be the format of data?
This question is too ill-posed to be answerable. You should use the format that's best for your purpose.