12,168 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
A superior edge preserving filter with a systematic analysis
A new, adaptive, edge preserving filter for use in image processing is presented. It had superior performance when compared to other filters. Termed the contiguous K-average, it aggregates pixels by examining all pixels contiguous to an existing cluster and adding the pixel closest to the mean of the existing cluster. The process is iterated until K pixels were accumulated. Rather than simply compare the visual results of processing with this operator to other filters, some approaches were developed which allow quantitative evaluation of how well and filter performs. Particular attention is given to the standard deviation of noise within a feature and the stability of imagery under iterative processing. Demonstrations illustrate the performance of several filters to discriminate against noise and retain edges, the effect of filtering as a preprocessing step, and the utility of the contiguous K-average filter when used with remote sensing data
One-shot Detail Retouching with Patch Space Neural Field based Transformation Blending
Photo retouching is a difficult task for novice users as it requires expert
knowledge and advanced tools. Photographers often spend a great deal of time
generating high-quality retouched photos with intricate details. In this paper,
we introduce a one-shot learning based technique to automatically retouch
details of an input image based on just a single pair of before and after
example images. Our approach provides accurate and generalizable detail edit
transfer to new images. We achieve these by proposing a new representation for
image to image maps. Specifically, we propose neural field based transformation
blending in the patch space for defining patch to patch transformations for
each frequency band. This parametrization of the map with anchor
transformations and associated weights, and spatio-spectral localized patches,
allows us to capture details well while staying generalizable. We evaluate our
technique both on known ground truth filtes and artist retouching edits. Our
method accurately transfers complex detail retouching edits
Image Denoising with Graph-Convolutional Neural Networks
Recovering an image from a noisy observation is a key problem in signal
processing. Recently, it has been shown that data-driven approaches employing
convolutional neural networks can outperform classical model-based techniques,
because they can capture more powerful and discriminative features. However,
since these methods are based on convolutional operations, they are only
capable of exploiting local similarities without taking into account non-local
self-similarities. In this paper we propose a convolutional neural network that
employs graph-convolutional layers in order to exploit both local and non-local
similarities. The graph-convolutional layers dynamically construct
neighborhoods in the feature space to detect latent correlations in the feature
maps produced by the hidden layers. The experimental results show that the
proposed architecture outperforms classical convolutional neural networks for
the denoising task.Comment: IEEE International Conference on Image Processing (ICIP) 201
Unsharp Mask Guided Filtering
The goal of this paper is guided image filtering, which emphasizes the
importance of structure transfer during filtering by means of an additional
guidance image. Where classical guided filters transfer structures using
hand-designed functions, recent guided filters have been considerably advanced
through parametric learning of deep networks. The state-of-the-art leverages
deep networks to estimate the two core coefficients of the guided filter. In
this work, we posit that simultaneously estimating both coefficients is
suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired
by unsharp masking, a classical technique for edge enhancement that requires
only a single coefficient, we propose a new and simplified formulation of the
guided filter. Our formulation enjoys a filtering prior from a low-pass filter
and enables explicit structure transfer by estimating a single coefficient.
Based on our proposed formulation, we introduce a successive guided filtering
network, which provides multiple filtering results from a single network,
allowing for a trade-off between accuracy and efficiency. Extensive ablations,
comparisons and analysis show the effectiveness and efficiency of our
formulation and network, resulting in state-of-the-art results across filtering
tasks like upsampling, denoising, and cross-modality filtering. Code is
available at \url{https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering}.Comment: IEEE Transactions on Image Processing, 202
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