Search
Search Results
-
HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks
Image distortion is an inevitable part of the image acquisition process, which negatively affects the high-frequency contents of the images....
-
Intermediate-term memory mechanism inspired lightweight single image super resolution
The essence of the Single Image Super Resolution (SISR) task revolves around learning and memorizing the map** relationship between low-resolution...
-
Research on Image Super Resolution Reconstruction Based on Deep Learning
To enhance the precision and clarity of graphic and image depictions, we propose a super-resolution image reconstruction method driven by the power...
-
Efficiently Amalgamated CNN-Transformer Network for Image Super-Resolution Reconstruction
Currently, heavy and sophisticated neural network models are designed to improve image super-resolution reconstruction accuracy. However, the model...
-
Dictionary learning-based image super-resolution for multimedia devices
In multimedia devices such as mobile phones, surveillance cameras, and web cameras, image sensors have limited spatial resolution. As a result, the...
-
Rt-swinir: an improved digital wallchart image super-resolution with attention-based learned text loss
In recent years, image super-resolution (SR) has made remarkable progress in areas such as natural images or text images. However, in the field of...
-
FISRCN: a single small-sized image super-resolution convolutional neural network by using edge detection
In recent years, deep neural network-based models have shown remarkable success in achieving high-quality reconstruction for single image...
-
Improving Text Image Super-Resolution Using Optimal Transport
Text images in natural scene captured by handheld devices like mobile phone are usually faced with low resolution problems, making optical character...
-
Transpose convolution based model for super-resolution image reconstruction
Single image resolution is a noticeably challenging issue that targets to acquire a high-resolution output out of one of its low-resolution variants....
-
HST: Hierarchical Swin Transformer for Compressed Image Super-Resolution
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and...
-
Downsampling consistency correction-based quality enhancement for CNN-based light field image super-resolution
In recent years, numerous CNN-based light field (LF) image super-resolution (SR) methods have been developed. However, due to the downsampling...
-
Supporting ANFIS interpolation for image super resolution with fuzzy rough feature selection
Image Super-Resolution (ISR) is utilised to generate a high-resolution image from a low-resolution one. However, most current techniques for ISR...
-
Fingerprint image super-resolution based on multi-class deep dictionary learning and ridge prior
The identification of low-resolution fingerprints has always been one of the focuses in the field of biometric identification. This paper proposes a...
-
A contrastive learning-based iterative network for remote sensing image super-resolution
Many deep convolutional neural network(CNN)-based methods have achieved significant success in noise-free image super-resolution(SR) tasks. However,...
-
Relation-consistency graph convolutional network for image super-resolution
Convolutional neural networks (CNNs) have been widely exploited in single image super-resolution (SISR) due to their powerful feature representation...
-
Memory-Augmented Deep Unfolding Network for Guided Image Super-resolution
Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR)...
-
Learning cascade regression for super-resolution image quality assessment
Super-resolution (SR) image quality assessment (SRIQA) is a fundamental topic in the literature of SR domain. Most existing SR methods usually adopt...
-
High-frequency channel attention and contrastive learning for image super-resolution
Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general,...
-
Residual aggregation U-shaped network for image super-resolution
Recent research on image super-resolution (SR) task has greatly progressed with the development of convolutional neural networks (CNNs). Most...
-
Multi-granularity Transformer for Image Super-Resolution
Recently, transformers have made great success in computer vision. Thus far, most of those works focus on high-level tasks, e.g., image...