Search Results for author:
Found 8 papers, 5 papers with code
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Perceptual Image Quality Assessment with Transformers
In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task.
Texture Transform Attention for Realistic Image Inpainting
Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers.
Ranked #27 on
Image Super-Resolution
on BSD100 - 4x upscaling
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths.
Ranked #28 on
Image Super-Resolution
on BSD100 - 4x upscaling
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively.
Ranked #51 on
Image Super-Resolution
on BSD100 - 4x upscaling
Generative adversarial network-based image super-resolution using perceptual content losses
In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion.
