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A review of single image super-resolution reconstruction based on deep learning
M Yu, J Shi, C Xue, X Hao, G Yan - Multimedia Tools and Applications, 2024 - Springer
Single image super-resolution (SISR) is an important research field in computer vision, the
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …
Diffir: Efficient diffusion model for image restoration
Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …
process into a sequential application of a denoising network. However, different from image …
A hybrid network of cnn and transformer for lightweight image super-resolution
J Fang, H Lin, X Chen, K Zeng - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …
super-resolution. However, these existing architectures commonly build massive number of …
Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution
Transformer-based methods have exhibited remarkable potential in single image super-
resolution (SISR) by effectively extracting long-range dependencies. However, most of the …
resolution (SISR) by effectively extracting long-range dependencies. However, most of the …
Vmambair: Visual state space model for image restoration
Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …
images from degraded inputs. Various models, such as convolutional neural networks …
HCLR-Net: hybrid contrastive learning regularization with locally randomized perturbation for underwater image enhancement
Underwater image enhancement presents a significant challenge due to the complex and
diverse underwater environments that result in severe degradation phenomena such as light …
diverse underwater environments that result in severe degradation phenomena such as light …
Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration
Fluorescence microscopy-based image restoration has received widespread attention in the
life sciences and has led to significant progress, benefiting from deep learning technology …
life sciences and has led to significant progress, benefiting from deep learning technology …
Multi-scale attention network for single image super-resolution
ConvNets can compete with transformers in high-level tasks by exploiting larger receptive
fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale …
fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale …
Osffnet: Omni-stage feature fusion network for lightweight image super-resolution
Y Wang, T Zhang - Proceedings of the AAAI conference on artificial …, 2024 - ojs.aaai.org
Recently, several lightweight methods have been proposed to implement single-image
super-resolution (SISR) on resource-constrained devices. However, these methods primarily …
super-resolution (SISR) on resource-constrained devices. However, these methods primarily …
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 …