1,015 research outputs found
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
Generative modeling over natural images is one of the most fundamental
machine learning problems. However, few modern generative models, including
Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued
images that are frequently encountered in real-world applications. To fill the
gap, this paper first formulates the problem of generating manifold-valued
images and exploits three typical instances: hue-saturation-value (HSV) color
image generation, chromaticity-brightness (CB) color image generation, and
diffusion-tensor (DT) image generation. For the proposed generative modeling
problem, we then introduce a theorem of optimal transport to derive a new
Wasserstein distance of data distributions on complete manifolds, enabling us
to achieve a tractable objective under the WGAN framework. In addition, we
recommend three benchmark datasets that are CIFAR-10 HSV/CB color images,
ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we
experimentally demonstrate the proposed manifold-aware WGAN model can generate
more plausible manifold-valued images than its competitors.Comment: Accepted by AAAI 201
Inner and Inter Label Propagation: Salient Object Detection in the Wild
In this paper, we propose a novel label propagation based method for saliency
detection. A key observation is that saliency in an image can be estimated by
propagating the labels extracted from the most certain background and object
regions. For most natural images, some boundary superpixels serve as the
background labels and the saliency of other superpixels are determined by
ranking their similarities to the boundary labels based on an inner propagation
scheme. For images of complex scenes, we further deploy a 3-cue-center-biased
objectness measure to pick out and propagate foreground labels. A
co-transduction algorithm is devised to fuse both boundary and objectness
labels based on an inter propagation scheme. The compactness criterion decides
whether the incorporation of objectness labels is necessary, thus greatly
enhancing computational efficiency. Results on five benchmark datasets with
pixel-wise accurate annotations show that the proposed method achieves superior
performance compared with the newest state-of-the-arts in terms of different
evaluation metrics.Comment: The full version of the TIP 2015 publicatio
Face Aging via Diffusion-based Editing
In this paper, we address the problem of face aging: generating past or
future facial images by incorporating age-related changes to the given face.
Previous aging methods rely solely on human facial image datasets and are thus
constrained by their inherent scale and bias. This restricts their application
to a limited generatable age range and the inability to handle large age gaps.
We propose FADING, a novel approach to address Face Aging via DIffusion-based
editiNG. We go beyond existing methods by leveraging the rich prior of
large-scale language-image diffusion models. First, we specialize a pre-trained
diffusion model for the task of face age editing by using an age-aware
fine-tuning scheme. Next, we invert the input image to latent noise and obtain
optimized null text embeddings. Finally, we perform text-guided local age
editing via attention control. The quantitative and qualitative analyses
demonstrate that our method outperforms existing approaches with respect to
aging accuracy, attribute preservation, and aging quality.Comment: accepted at BMVC 202
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