Search Results for author:
Found 14 papers, 10 papers with code
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
Depending upon whether using a discriminator or not, a deep convolutional neural network can provide an image with high fidelity or better perceptual quality.
Deep Relighting Networks for Image Light Source Manipulation
Manipulating the light source of given images is an interesting task and useful in various applications, including photography and cinematography.
Ranked #2 on
Image Relighting
on VIDIT’20 validation set
Image Super-Resolution via Attention based Back Projection Networks
In this paper, we propose an Attention based Back Projection Network (ABPN) for image super-resolution.
Ranked #1 on
Image Super-Resolution
on Manga109 - 16x upscaling
DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting
Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas.
Variational AutoEncoder for Reference based Image Super-Resolution
In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE).
Hierarchical Back Projection Network for Image Super-Resolution
We propose a Hierarchical Back Projection Network (HBPN), that cascades multiple HourGlass (HG) modules to bottom-up and top-down process features across all scales to capture various spatial correlations and then consolidates the best representation for reconstruction.
Ranked #4 on
Image Super-Resolution
on BSD100 - 8x upscaling
Name Your Style: An Arbitrary Artist-aware Image Style Transfer
Moreover, it can mimic the styles of one or many artists to achieve attractive results, thus highlighting a promising direction in image style transfer.
SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval
With the success of deep learning methods in many image processing tasks, deep learning approaches have also been introduced to the phase retrieval problem recently.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise
In this paper, we propose a robust bone feature segmentation method based on the U-net structure for ultrasound spine Volume Projection Imaging (VPI) images.
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network
The results demonstrate the superiority and practicality of the proposed PPRNet over the traditional learning-based PR methods.
Multiple-level Green Noise Mask Design for Practical Fourier Phase Retrieval
This paper proposes a two-stage optimization algorithm to generate multi-level random masks named $\textit{OptMask}$ that can also significantly reduce high-frequency components in the measurements but achieve higher accuracy than the binary masking scheme.
