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A review on Single Image Super Resolution techniques using generative adversarial network
Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …
and refined details from a low resolution (LR) image to get upscaled and sharper high …
Learning texture transformer network for image super-resolution
We study on image super-resolution (SR), which aims to recover realistic textures from a low-
resolution (LR) image. Recent progress has been made by taking high-resolution images as …
resolution (LR) image. Recent progress has been made by taking high-resolution images as …
Image super-resolution by neural texture transfer
Due to the significant information loss in low-resolution (LR) images, it has become
extremely challenging to further advance the state-of-the-art of single image super …
extremely challenging to further advance the state-of-the-art of single image super …
Photo-realistic single image super-resolution using a generative adversarial network
Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …
faster and deeper convolutional neural networks, one central problem remains largely …
Enhancenet: Single image super-resolution through automated texture synthesis
Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
Task-driven super resolution: Object detection in low-resolution images
We consider how image super-resolution (SR) can contribute to an object detection task in
low-resolution images. Intuitively, SR gives a positive impact on the object detection task …
low-resolution images. Intuitively, SR gives a positive impact on the object detection task …
Robust reference-based super-resolution with similarity-aware deformable convolution
In this paper, we propose a novel and efficient reference feature extraction module referred
to as the Similarity Search and Extraction Network (SSEN) for reference-based super …
to as the Similarity Search and Extraction Network (SSEN) for reference-based super …
Symmetric parallax attention for stereo image super-resolution
Although recent years have witnessed the great advances in stereo image super-resolution
(SR), the beneficial information provided by binocular systems has not been fully used …
(SR), the beneficial information provided by binocular systems has not been fully used …
RRSGAN: Reference-based super-resolution for remote sensing image
Remote sensing image super-resolution (SR) plays an important role by supplementing the
lack of original high-resolution (HR) images in the study scenarios of large spatial areas or …
lack of original high-resolution (HR) images in the study scenarios of large spatial areas or …
A systematic survey of deep learning-based single-image super-resolution
Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …