47 research outputs found
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.Comment: In IJCV 201
Continuous Facial Motion Deblurring
We introduce a novel framework for continuous facial motion deblurring that
restores the continuous sharp moment latent in a single motion-blurred face
image via a moment control factor. Although a motion-blurred image is the
accumulated signal of continuous sharp moments during the exposure time, most
existing single image deblurring approaches aim to restore a fixed number of
frames using multiple networks and training stages. To address this problem, we
propose a continuous facial motion deblurring network based on GAN (CFMD-GAN),
which is a novel framework for restoring the continuous moment latent in a
single motion-blurred face image with a single network and a single training
stage. To stabilize the network training, we train the generator to restore
continuous moments in the order determined by our facial motion-based
reordering process (FMR) utilizing domain-specific knowledge of the face.
Moreover, we propose an auxiliary regressor that helps our generator produce
more accurate images by estimating continuous sharp moments. Furthermore, we
introduce a control-adaptive (ContAda) block that performs spatially deformable
convolution and channel-wise attention as a function of the control factor.
Extensive experiments on the 300VW datasets demonstrate that the proposed
framework generates a various number of continuous output frames by varying the
moment control factor. Compared with the recent single-to-single image
deblurring networks trained with the same 300VW training set, the proposed
method show the superior performance in restoring the central sharp frame in
terms of perceptual metrics, including LPIPS, FID and Arcface identity
distance. The proposed method outperforms the existing single-to-video
deblurring method for both qualitative and quantitative comparisons
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
Survey on Controlable Image Synthesis with Deep Learning
Image synthesis has attracted emerging research interests in academic and
industry communities. Deep learning technologies especially the generative
models greatly inspired controllable image synthesis approaches and
applications, which aim to generate particular visual contents with latent
prompts. In order to further investigate low-level controllable image synthesis
problem which is crucial for fine image rendering and editing tasks, we present
a survey of some recent works on 3D controllable image synthesis using deep
learning. We first introduce the datasets and evaluation indicators for 3D
controllable image synthesis. Then, we review the state-of-the-art research for
geometrically controllable image synthesis in two aspects: 1)
Viewpoint/pose-controllable image synthesis; 2) Structure/shape-controllable
image synthesis. Furthermore, the photometrically controllable image synthesis
approaches are also reviewed for 3D re-lighting researches. While the emphasis
is on 3D controllable image synthesis algorithms, the related applications,
products and resources are also briefly summarized for practitioners.Comment: 19 pages, 17 figure
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Latest diffusion-based methods for many image restoration tasks outperform
traditional models, but they encounter the long-time inference problem. To
tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM) with an
Efficient Conditional Sampling (ECS) strategy. WaveDM learns the distribution
of clean images in the wavelet domain conditioned on the wavelet spectrum of
degraded images after wavelet transform, which is more time-saving in each step
of sampling than modeling in the spatial domain. In addition, ECS follows the
same procedure as the deterministic implicit sampling in the initial sampling
period and then stops to predict clean images directly, which reduces the
number of total sampling steps to around 5. Evaluations on four benchmark
datasets including image raindrop removal, defocus deblurring, demoir\'eing,
and denoising demonstrate that WaveDM achieves state-of-the-art performance
with the efficiency that is comparable to traditional one-pass methods and over
100 times faster than existing image restoration methods using vanilla
diffusion models