339 research outputs found
A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing
The goal of edge-histogram specification is to find an image whose edge image
has a histogram that matches a given edge-histogram as much as possible.
Mignotte has proposed a non-convex model for the problem [M. Mignotte. An
energy-based model for the image edge-histogram specification problem. IEEE
Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge
magnitudes of an input image are first modified by histogram specification to
match the given edge-histogram. Then, a non-convex model is minimized to find
an output image whose edge-histogram matches the modified edge-histogram. The
non-convexity of the model hinders the computations and the inclusion of useful
constraints such as the dynamic range constraint. In this paper, instead of
considering edge magnitudes, we directly consider the image gradients and
propose a convex model based on them. Furthermore, we include additional
constraints in our model based on different applications. The convexity of our
model allows us to compute the output image efficiently using either
Alternating Direction Method of Multipliers or Fast Iterative
Shrinkage-Thresholding Algorithm. We consider several applications in
edge-preserving smoothing including image abstraction, edge extraction, details
exaggeration, and documents scan-through removal. Numerical results are given
to illustrate that our method successfully produces decent results efficiently
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Blind face restoration is a highly ill-posed problem that often requires
auxiliary guidance to 1) improve the mapping from degraded inputs to desired
outputs, or 2) complement high-quality details lost in the inputs. In this
paper, we demonstrate that a learned discrete codebook prior in a small proxy
space largely reduces the uncertainty and ambiguity of restoration mapping by
casting blind face restoration as a code prediction task, while providing rich
visual atoms for generating high-quality faces. Under this paradigm, we propose
a Transformer-based prediction network, named CodeFormer, to model the global
composition and context of the low-quality faces for code prediction, enabling
the discovery of natural faces that closely approximate the target faces even
when the inputs are severely degraded. To enhance the adaptiveness for
different degradation, we also propose a controllable feature transformation
module that allows a flexible trade-off between fidelity and quality. Thanks to
the expressive codebook prior and global modeling, CodeFormer outperforms the
state of the arts in both quality and fidelity, showing superior robustness to
degradation. Extensive experimental results on synthetic and real-world
datasets verify the effectiveness of our method.Comment: Accepted by NeurIPS 2022. Code: https://github.com/sczhou/CodeForme
Understanding Deformable Alignment in Video Super-Resolution
Deformable convolution, originally proposed for the adaptation to geometric
variations of objects, has recently shown compelling performance in aligning
multiple frames and is increasingly adopted for video super-resolution. Despite
its remarkable performance, its underlying mechanism for alignment remains
unclear. In this study, we carefully investigate the relation between
deformable alignment and the classic flow-based alignment. We show that
deformable convolution can be decomposed into a combination of spatial warping
and convolution. This decomposition reveals the commonality of deformable
alignment and flow-based alignment in formulation, but with a key difference in
their offset diversity. We further demonstrate through experiments that the
increased diversity in deformable alignment yields better-aligned features, and
hence significantly improves the quality of video super-resolution output.
Based on our observations, we propose an offset-fidelity loss that guides the
offset learning with optical flow. Experiments show that our loss successfully
avoids the overflow of offsets and alleviates the instability problem of
deformable alignment. Aside from the contributions to deformable alignment, our
formulation inspires a more flexible approach to introduce offset diversity to
flow-based alignment, improving its performance.Comment: Tech report, 15 pages, 19 figure
Dual Associated Encoder for Face Restoration
Restoring facial details from low-quality (LQ) images has remained a
challenging problem due to its ill-posedness induced by various degradations in
the wild. The existing codebook prior mitigates the ill-posedness by leveraging
an autoencoder and learned codebook of high-quality (HQ) features, achieving
remarkable quality. However, existing approaches in this paradigm frequently
depend on a single encoder pre-trained on HQ data for restoring HQ images,
disregarding the domain gap between LQ and HQ images. As a result, the encoding
of LQ inputs may be insufficient, resulting in suboptimal performance. To
tackle this problem, we propose a novel dual-branch framework named DAEFR. Our
method introduces an auxiliary LQ branch that extracts crucial information from
the LQ inputs. Additionally, we incorporate association training to promote
effective synergy between the two branches, enhancing code prediction and
output quality. We evaluate the effectiveness of DAEFR on both synthetic and
real-world datasets, demonstrating its superior performance in restoring facial
details.Comment: Technical Repor
Impact of smoking on health system costs among cancer patients in a retrospective cohort study in Ontario, Canada
Objective Smoking is the main modifiable cancer risk factor. The objective of this study was to examine the impact of smoking on health system costs among newly diagnosed adult patients with cancer. Specifically, costs of patients with cancer who were current smokers were compared with those of non-smokers from a publicly funded health system perspective. Methods This population-based cohort study of patients with cancer used administrative databases to identify smokers and non-smokers (1 April 2014-31 March 2016) and their healthcare costs in the 12-24 months following a cancer diagnosis. The health services included were hospitalisations, emergency room visits, drugs, home care services and physician services (from the time of diagnosis onwards). The difference in cost (ie, incremental cost) between patients with cancer who were smokers and those who were non-smokers was estimated using a generalised linear model (with log link and gamma distribution), and adjusted for age, sex, neighbourhood income, rurality, cancer site, cancer stage, geographical region and comorbidities. Results This study identified 3606 smokers and 14 911 non-smokers. Smokers were significantly younger (61 vs 65 years), more likely to be male (53%), lived in poorer neighbourhoods, had more advanced cancer stage,and were more likely to die within 1 year of diagnosis, compared with non-smokers. The regression model revealed that, on average, smokers had significantly higher monthly healthcare costs (4847), p<0.05. Conclusions Smoking status has a significant impact on healthcare costs among patients with cancer. On average, smokers incurred higher healthcare costs than non-smokers. These findings provide a further rationale for efforts to introduce evidence-based smoking cessation programmes as a standard of care for patients with cancer as they have the potential not only to improve patients' outcomes but also to reduce the economic burden of smoking on the healthcare system
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