In this paper, we proposed a multi-component loss function based on pixel-wise loss, perceptual loss and adversarial loss for a multi-scale feature mapping ...
In this paper, we proposed a multi-component loss function based on pixel-wise loss, perceptual loss and adversarial loss for a multi-scale feature mapping ...
In this paper, we proposed a multi-component loss function based on pixel-wise loss, perceptual loss and adversarial loss for a multi-scale feature mapping ...
Abstract. Image super-resolution reconstruction is one of the methods to improve resolution by learning the inherent features and attributes of images.
Single image super-resolution (SISR) aims to reconstruct a high-resolution image from a degraded low-resolution image. In recent years, the super-resolution ...
Image super-resolution (SR) is a widely concerned low-level computer vision task that focuses on rebuilding the missing high-frequency information from the ...
Missing: adversarial | Show results with:adversarial
Single-image super-resolution, aims to reconstruct images from low-resolution to high-resolution and super-high-resolution, so as to improve image clarity ...
People also ask
What is super-resolution reconstruction of an image?
What is the difference between single image and multi image super-resolution?
What is single image super-resolution?
Super-resolution (SR) is a technique that restores image details based on existing information, enhancing the resolution of images to prevent quality ...
Apr 5, 2024 · To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), ...
May 2, 2024 · Kong et al. [47] designed an efficient super-resolution reconstruction algorithm named Residual Local Feature Network (RLFN). Sun et al. [48] ...