Search Results for author:
Found 12 papers, 7 papers with code
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
In this paper, we propose an adversarial learning colorization approach coupled with semantic information.
Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition
In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images.
Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks
Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network.
Rethinking Motion Deblurring Training: A Segmentation-Based Method for Simulating Non-Uniform Motion Blurred Images
Successful training of end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images.
Event-based Image Deblurring with Dynamic Motion Awareness
Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself.
Automatic Flare Spot Artifact Detection and Removal in Photographs
Flare spot is one type of flare artifact caused by a number of conditions, frequently provoked by one or more high-luminance sources within or close to the camera field of view.
Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks
Image inpainting is the task of filling-in missing regions of a damaged or incomplete image.
Automatic Flare Spot Artifacts Detection and Removal in Photographs
Flare spot is one type of flare artifact caused by a number of conditions, frequently provoked by one or more high-luminance sources within or close to the camera field of view.
Influence of Color Spaces for Deep Learning Image Colorization
In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?".
Analysis of Different Losses for Deep Learning Image Colorization
While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output.
An Analysis of Generative Methods for Multiple Image Inpainting
In this work, we focus on learning-based image completion methods for multiple and diverse inpainting which goal is to provide a set of distinct solutions for a given damaged image.
EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction
The simulator permits the use of inexpensive cameras with long exposure to capture high-quality GS images.
