Search Results for author:
Found 6 papers, 2 papers with code
BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion
We propose BeyondScene, a novel framework that overcomes prior limitations, generating exquisite higher-resolution (over 8K) human-centric scenes with exceptional text-image correspondence and naturalness using existing pretrained diffusion models.
Detailed Human-Centric Text Description-Driven Large Scene Synthesis
Text-driven large scene image synthesis has made significant progress with diffusion models, but controlling it is challenging.
BlindHarmony: "Blind" Harmonization for MR Images via Flow model
These differences in images create a domain gap that needs to be bridged by a step called image harmonization, to process the images successfully using conventional or deep learning-based image analysis (e. g., segmentation).
Adaptive GLCM sampling for transformer-based COVID-19 detection on CT
The experimental results show that the proposed method improve the detection performance with large margin without much difficult modification to the model.
Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring
Secondly, we propose multi-blurring recurrent neural network (MBRNN) that can synthesize more blurred images from neighboring frames, yielding substantially improved performance with existing video deblurring methods.
Ranked #5 on
Deblurring
on DVD
(using extra training data)
Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training
Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales.
Ranked #14 on
Deblurring
on HIDE (trained on GOPRO)
