Super-Resolution
1270 papers with code • 0 benchmarks • 20 datasets
Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
( Credit: MemNet )
Benchmarks
These leaderboards are used to track progress in Super-Resolution
Datasets
MSU SR-QA Dataset
TextZoom
RealSRSet
DRealSR
Holopix50k
PROBA-V
Stanford Light Field
OST300
Subtasks
Latest papers with no code
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency.
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution
This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field.
SRGS: Super-Resolution 3D Gaussian Splatting
This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering.
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model
Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks.
Little Pilot is Needed for Channel Estimation with Integrated Super-Resolution Sensing and Communication
First, compared to the conventional channel estimation methods purely relying on channel training, much fewer parameters need to be estimated once the multi-path angles are accurately estimated.
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant.
Super-resolution of biomedical volumes with 2D supervision
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models.
Differentiable Search for Finding Optimal Quantization Strategy
To solve the issue, in this paper, we propose a differentiable quantization strategy search (DQSS) to assign optimal quantization strategy for individual layer by taking advantages of the benefits of different quantization algorithms.
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.
LIPT: Latency-aware Image Processing Transformer
Extensive experiments on multiple image processing tasks (e. g., image super-resolution (SR), JPEG artifact reduction, and image denoising) demonstrate the superiority of LIPT on both latency and PSNR.
