1,952 research outputs found
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
Recent years have witnessed the remarkable performance of diffusion models in
various vision tasks. However, for image restoration that aims to recover clear
images with sharper details from given degraded observations, diffusion-based
methods may fail to recover promising results due to inaccurate noise
estimation. Moreover, simple constraining noises cannot effectively learn
complex degradation information, which subsequently hinders the model capacity.
To solve the above problems, we propose a coarse-to-fine diffusion Transformer
(C2F-DFT) for image restoration. Specifically, our C2F-DFT contains diffusion
self-attention (DFSA) and diffusion feed-forward network (DFN) within a new
coarse-to-fine training scheme. The DFSA and DFN respectively capture the
long-range diffusion dependencies and learn hierarchy diffusion representation
to facilitate better restoration. In the coarse training stage, our C2F-DFT
estimates noises and then generates the final clean image by a sampling
algorithm. To further improve the restoration quality, we propose a simple yet
effective fine training scheme. It first exploits the coarse-trained diffusion
model with fixed steps to generate restoration results, which then would be
constrained with corresponding ground-truth ones to optimize the models to
remedy the unsatisfactory results affected by inaccurate noise estimation.
Extensive experiments show that C2F-DFT significantly outperforms
diffusion-based restoration method IR-SDE and achieves competitive performance
compared with Transformer-based state-of-the-art methods on tasks,
including deraining, deblurring, and real denoising.Comment: 9 pages, 8 figure
A Dive into SAM Prior in Image Restoration
The goal of image restoration (IR), a fundamental issue in computer vision,
is to restore a high-quality (HQ) image from its degraded low-quality (LQ)
observation. Multiple HQ solutions may correspond to an LQ input in this poorly
posed problem, creating an ambiguous solution space. This motivates the
investigation and incorporation of prior knowledge in order to effectively
constrain the solution space and enhance the quality of the restored images. In
spite of the pervasive use of hand-crafted and learned priors in IR, limited
attention has been paid to the incorporation of knowledge from large-scale
foundation models. In this paper, we for the first time leverage the prior
knowledge of the state-of-the-art segment anything model (SAM) to boost the
performance of existing IR networks in an parameter-efficient tuning manner. In
particular, the choice of SAM is based on its robustness to image degradations,
such that HQ semantic masks can be extracted from it. In order to leverage
semantic priors and enhance restoration quality, we propose a lightweight SAM
prior tuning (SPT) unit. This plug-and-play component allows us to effectively
integrate semantic priors into existing IR networks, resulting in significant
improvements in restoration quality. As the only trainable module in our
method, the SPT unit has the potential to improve both efficiency and
scalability. We demonstrate the effectiveness of the proposed method in
enhancing a variety of methods across multiple tasks, such as image
super-resolution and color image denoising.Comment: Technical Repor
Generative Modeling in Structural-Hankel Domain for Color Image Inpainting
In recent years, some researchers focused on using a single image to obtain a
large number of samples through multi-scale features. This study intends to a
brand-new idea that requires only ten or even fewer samples to construct the
low-rank structural-Hankel matrices-assisted score-based generative model
(SHGM) for color image inpainting task. During the prior learning process, a
certain amount of internal-middle patches are firstly extracted from several
images and then the structural-Hankel matrices are constructed from these
patches. To better apply the score-based generative model to learn the internal
statistical distribution within patches, the large-scale Hankel matrices are
finally folded into the higher dimensional tensors for prior learning. During
the iterative inpainting process, SHGM views the inpainting problem as a
conditional generation procedure in low-rank environment. As a result, the
intermediate restored image is acquired by alternatively performing the
stochastic differential equation solver, alternating direction method of
multipliers, and data consistency steps. Experimental results demonstrated the
remarkable performance and diversity of SHGM.Comment: 11 pages, 10 figure
Mini-Workshop: Analytical and Numerical Methods in Image and Surface Processing
The workshop successfully brought together researchers from mathematical analysis, numerical mathematics, computer graphics and image processing. The focus was on variational methods in image and surface processing such as active contour models, Mumford-Shah type functionals, image and surface denoising based on geometric evolution problems in image and surface fairing, physical modeling of surfaces, the restoration of images and surfaces using higher order variational formulations
DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence
Removing soft and self shadows that lack clear boundaries from a single image
is still challenging. Self shadows are shadows that are cast on the object
itself. Most existing methods rely on binary shadow masks, without considering
the ambiguous boundaries of soft and self shadows. In this paper, we present
DeS3, a method that removes hard, soft and self shadows based on the self-tuned
ViT feature similarity and color convergence. Our novel ViT similarity loss
utilizes features extracted from a pre-trained Vision Transformer. This loss
helps guide the reverse diffusion process towards recovering scene structures.
We also introduce a color convergence loss to constrain the surface colors in
the reverse inference process to avoid any color shifts. Our DeS3 is able to
differentiate shadow regions from the underlying objects, as well as shadow
regions from the object casting the shadow. This capability enables DeS3 to
better recover the structures of objects even when they are partially occluded
by shadows. Different from existing methods that rely on constraints during the
training phase, we incorporate the ViT similarity and color convergence loss
during the sampling stage. This enables our DeS3 model to effectively integrate
its strong modeling capabilities with input-specific knowledge in a self-tuned
manner. Our method outperforms state-of-the-art methods on the SRD, AISTD,
LRSS, USR and UIUC datasets, removing hard, soft, and self shadows robustly.
Specifically, our method outperforms the SOTA method by 20% of the RMSE of the
whole image on the SRD dataset
A multigrid platform for real-time motion computation with discontinuity-preserving variational methods
Variational methods are among the most accurate techniques for estimating the optic flow. They yield dense flow fields and can be designed such that they preserve discontinuities, allow to deal with large displacements and perform well under noise or varying illumination. However, such adaptations render the minimisation of the underlying energy functional very expensive in terms of computational costs: Typically, one or more large linear or nonlinear systems of equations have to be solved in order to obtain the desired solution. Consequently, variational methods are considered to be too slow for real-time performance. In our paper we address this problem in two ways: (i) We present a numerical framework based on bidirectional multigrid methods for accelerating a broad class of variational optic flow methods with different constancy and smoothness assumptions. In particular, discontinuity-preserving regularisation strategies are thereby in the focus of our work. (ii) We show by the examples of classical as well as more advanced variational techniques that real-time performance is possible - even for very complex optic flow models with high accuracy. Experiments show frame rates up to 63 dense flow fields per second for real-world image sequences of size 160 x 120 on a standard PC. Compared to classical iterative methods this constitutes a speedup of two to four orders of magnitude
AMPA Receptor Phosphorylation and Synaptic Colocalization on Motor Neurons Drive Maladaptive Plasticity below Complete Spinal Cord Injury.
Clinical spinal cord injury (SCI) is accompanied by comorbid peripheral injury in 47% of patients. Human and animal modeling data have shown that painful peripheral injuries undermine long-term recovery of locomotion through unknown mechanisms. Peripheral nociceptive stimuli induce maladaptive synaptic plasticity in dorsal horn sensory systems through AMPA receptor (AMPAR) phosphorylation and trafficking to synapses. Here we test whether ventral horn motor neurons in rats demonstrate similar experience-dependent maladaptive plasticity below a complete SCI in vivo. Quantitative biochemistry demonstrated that intermittent nociceptive stimulation (INS) rapidly and selectively increases AMPAR subunit GluA1 serine 831 phosphorylation and localization to synapses in the injured spinal cord, while reducing synaptic GluA2. These changes predict motor dysfunction in the absence of cell death signaling, suggesting an opportunity for therapeutic reversal. Automated confocal time-course analysis of lumbar ventral horn motor neurons confirmed a time-dependent increase in synaptic GluA1 with concurrent decrease in synaptic GluA2. Optical fractionation of neuronal plasma membranes revealed GluA2 removal from extrasynaptic sites on motor neurons early after INS followed by removal from synapses 2 h later. As GluA2-lacking AMPARs are canonical calcium-permeable AMPARs (CP-AMPARs), their stimulus- and time-dependent insertion provides a therapeutic target for limiting calcium-dependent dynamic maladaptive plasticity after SCI. Confirming this, a selective CP-AMPAR antagonist protected against INS-induced maladaptive spinal plasticity, restoring adaptive motor responses on a sensorimotor spinal training task. These findings highlight the critical involvement of AMPARs in experience-dependent spinal cord plasticity after injury and provide a pharmacologically targetable synaptic mechanism by which early postinjury experience shapes motor plasticity
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