Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution
Super-resolution (SR) techniques designed for real-world applications commonly encounter
two primary challenges: generalization performance and restoration accuracy. We …
two primary challenges: generalization performance and restoration accuracy. We …
Rethinking image super resolution from long-tailed distribution learning perspective
Existing studies have empirically observed that the resolution of the low-frequency region is
easier to enhance than that of the high-frequency one. Although plentiful works have been …
easier to enhance than that of the high-frequency one. Although plentiful works have been …
Lightweight image super-resolution based on deep learning: State-of-the-art and future directions
Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …
more and more attention, aiming to improve the images and videos resolutions. Most of the …
Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction
High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …
computational photography, autonomous driving, etc. However, the vast computational cost …
Spatially-adaptive feature modulation for efficient image super-resolution
Although deep learning-based solutions have achieved impressive reconstruction
performance in image super-resolution (SR), these models are generally large, with …
performance in image super-resolution (SR), these models are generally large, with …
Welder: Scheduling deep learning memory access via tile-graph
With the growing demand for processing higher fidelity data and the use of faster computing
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …
Large kernel distillation network for efficient single image super-resolution
Efficient and lightweight single-image super-resolution (SISR) has achieved remarkable
performance in recent years. One effective approach is the use of large kernel designs …
performance in recent years. One effective approach is the use of large kernel designs …
A simple transformer-style network for lightweight image super-resolution
The task of single image super resolution (SISR) has taken much attention in the last few
years due to the wide range of real-world applications. However, most of the recently …
years due to the wide range of real-world applications. However, most of the recently …
Udc-unet: Under-display camera image restoration via u-shape dynamic network
Abstract Under-Display Camera (UDC) has been widely exploited to help smartphones
realize full-screen displays. However, as the screen could inevitably affect the light …
realize full-screen displays. However, as the screen could inevitably affect the light …
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 …