Search Results for author:
Found 12 papers, 7 papers with code
IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation
Generative object compositing emerges as a promising new avenue for compositional image editing.
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model
A significant volume of analog information, i. e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in the digital world.
Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer
In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme.
ObjectStitch: Object Compositing With Diffusion Model
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results.
ObjectStitch: Generative Object Compositing
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results.
Layered Depth Refinement with Mask Guidance
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations.
Ranked #3 on
Blind Super-Resolution
on DIV2KRK - 2x upscaling
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video
Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications.
3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks
In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames.
