106 research outputs found
Toward Minimal-Sufficiency in Regression Tasks: An Approach Based on a Variational Estimation Bottleneck
We propose a new variational estimation bottleneck based on a mean-squared error metric to aid regression tasks. In particular, this bottleneck - which draws inspiration from a variational information bottleneck for classification counterparts - consists of two components: (1) one captures the notion of Vr -sufficiency that quantifies the ability for an estimator in some class of estimators Vr to infer the quantity of interest; (2) the other component appears to capture a notion of Vr - minimality that quantifies the ability of the estimator to generalize to new data. We demonstrate how to train this bottleneck for regression problems. We also conduct various experiments in image denoising and deraining applications showcasing that our proposed approach can lead to neural network regressors offering better performance without suffering from overfitting
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images
Removing multiple degradations, such as haze, rain, and blur, from real-world
images poses a challenging and illposed problem. Recently, unified models that
can handle different degradations have been proposed and yield promising
results. However, these approaches focus on synthetic images and experience a
significant performance drop when applied to realworld images. In this paper,
we introduce Uni-Removal, a twostage semi-supervised framework for addressing
the removal of multiple degradations in real-world images using a unified model
and parameters. In the knowledge transfer stage, Uni-Removal leverages a
supervised multi-teacher and student architecture in the knowledge transfer
stage to facilitate learning from pretrained teacher networks specialized in
different degradation types. A multi-grained contrastive loss is introduced to
enhance learning from feature and image spaces. In the domain adaptation stage,
unsupervised fine-tuning is performed by incorporating an adversarial
discriminator on real-world images. The integration of an extended
multi-grained contrastive loss and generative adversarial loss enables the
adaptation of the student network from synthetic to real-world domains.
Extensive experiments on real-world degraded datasets demonstrate the
effectiveness of our proposed method. We compare our Uni-Removal framework with
state-of-the-art supervised and unsupervised methods, showcasing its promising
results in real-world image dehazing, deraining, and deblurring simultaneously
RainDiffusion:When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining
What will happen when unsupervised learning meets diffusion models for
real-world image deraining? To answer it, we propose RainDiffusion, the first
unsupervised image deraining paradigm based on diffusion models. Beyond the
traditional unsupervised wisdom of image deraining, RainDiffusion introduces
stable training of unpaired real-world data instead of weakly adversarial
training. RainDiffusion consists of two cooperative branches: Non-diffusive
Translation Branch (NTB) and Diffusive Translation Branch (DTB). NTB exploits a
cycle-consistent architecture to bypass the difficulty in unpaired training of
standard diffusion models by generating initial clean/rainy image pairs. DTB
leverages two conditional diffusion modules to progressively refine the desired
output with initial image pairs and diffusive generative prior, to obtain a
better generalization ability of deraining and rain generation. Rain-Diffusion
is a non adversarial training paradigm, serving as a new standard bar for
real-world image deraining. Extensive experiments confirm the superiority of
our RainDiffusion over un/semi-supervised methods and show its competitive
advantages over fully-supervised ones.Comment: 9 page
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Image restoration in adverse weather conditions is a difficult task in
computer vision. In this paper, we propose a novel transformer-based framework
called GridFormer which serves as a backbone for image restoration under
adverse weather conditions. GridFormer is designed in a grid structure using a
residual dense transformer block, and it introduces two core designs. First, it
uses an enhanced attention mechanism in the transformer layer. The mechanism
includes stages of the sampler and compact self-attention to improve
efficiency, and a local enhancement stage to strengthen local information.
Second, we introduce a residual dense transformer block (RDTB) as the final
GridFormer layer. This design further improves the network's ability to learn
effective features from both preceding and current local features. The
GridFormer framework achieves state-of-the-art results on five diverse image
restoration tasks in adverse weather conditions, including image deraining,
dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The
source code and pre-trained models will be released.Comment: 17 pages, 12 figure
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