7,938 research outputs found
Deep Bilateral Learning for Real-Time Image Enhancement
Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201
Learning to Hallucinate Face Images via Component Generation and Enhancement
We propose a two-stage method for face hallucination. First, we generate
facial components of the input image using CNNs. These components represent the
basic facial structures. Second, we synthesize fine-grained facial structures
from high resolution training images. The details of these structures are
transferred into facial components for enhancement. Therefore, we generate
facial components to approximate ground truth global appearance in the first
stage and enhance them through recovering details in the second stage. The
experiments demonstrate that our method performs favorably against
state-of-the-art methodsComment: IJCAI 2017. Project page:
http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm
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