764 research outputs found
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
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Despite a rapid rise in the quality of built-in smartphone cameras, their
physical limitations - small sensor size, compact lenses and the lack of
specific hardware, - impede them to achieve the quality results of DSLR
cameras. In this work we present an end-to-end deep learning approach that
bridges this gap by translating ordinary photos into DSLR-quality images. We
propose learning the translation function using a residual convolutional neural
network that improves both color rendition and image sharpness. Since the
standard mean squared loss is not well suited for measuring perceptual image
quality, we introduce a composite perceptual error function that combines
content, color and texture losses. The first two losses are defined
analytically, while the texture loss is learned in an adversarial fashion. We
also present DPED, a large-scale dataset that consists of real photos captured
from three different phones and one high-end reflex camera. Our quantitative
and qualitative assessments reveal that the enhanced image quality is
comparable to that of DSLR-taken photos, while the methodology is generalized
to any type of digital camera
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
- …