433 research outputs found
A nonlinear image restoration framework using vector quantization
Vector quantization (VQ) is a powerful method used primarily in signal and image compression. In recent years, it has also been applied to various other image processing tasks, including image classification, histogram modification, and restoration. In this paper, we focus our attention on image restoration using VQ. We present a general framework that incorporates two other methods in the literature, and discuss our method that follows more naturally from this framework. With appropriate training data for the VQ codebook, this method can restore images beyond its diffraction limit. © 2004 IEEE.published_or_final_versio
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uniform
motion blur from a single blurry image. We propose a deep learning approach to
predicting the probabilistic distribution of motion blur at the patch level
using a convolutional neural network (CNN). We further extend the candidate set
of motion kernels predicted by the CNN using carefully designed image
rotations. A Markov random field model is then used to infer a dense
non-uniform motion blur field enforcing motion smoothness. Finally, motion blur
is removed by a non-uniform deblurring model using patch-level image prior.
Experimental evaluations show that our approach can effectively estimate and
remove complex non-uniform motion blur that is not handled well by previous
approaches.Comment: This is a final version accepted by CVPR 201
Generative Prior for Unsupervised Image Restoration
The challenge of restoring real world low-quality images is due to a lack of appropriate training data and difficulty in determining how the image was degraded. Recently, generative models have demonstrated great potential for creating high- quality images by utilizing the rich and diverse information contained within the model’s trained weights and learned latent representations. One popular type of generative model is the generative adversarial network (GAN). Many new methods have been developed to harness the information found in GANs for image manipulation. Our proposed approach is to utilize generative models for both understanding the degradation of an image and restoring it. We propose using a combination of cycle consistency losses and self-attention to enhance face images by first learning the degradation and then using this information to train a style-based neural network. We also aim to use the latent representation to achieve a high level of magnification for face images (x64). By incorporating the weights of a pre-trained StyleGAN into a restoration network with a vision transformer layer, we hope to improve the current state-of-the-art in face image restoration. Finally, we present a projection-based image-denoising algorithm named Noise2Code in the latent space of the VQGAN model with a fixed-point regularization strategy. The fixed-point condition follows the observation that the pre-trained VQGAN affects the clean and noisy images in a drastically different way. Unlike previous projection-based image restoration in the latent space, both the denoising network and VQGAN model parameters are jointly trained, although the latter is not needed during the testing. We report experimental results to demonstrate that the proposed Noise2Code approach is conceptually simple, computationally efficient, and generalizable to real-world degradation scenarios
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Existing dehazing approaches struggle to process real-world hazy images owing
to the lack of paired real data and robust priors. In this work, we present a
new paradigm for real image dehazing from the perspectives of synthesizing more
realistic hazy data and introducing more robust priors into the network.
Specifically, (1) instead of adopting the de facto physical scattering model,
we rethink the degradation of real hazy images and propose a phenomenological
pipeline considering diverse degradation types. (2) We propose a Real Image
Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is
pre-trained on a large-scale high-quality dataset to obtain the discrete
codebook, encapsulating high-quality priors (HQPs). After replacing the
negative effects brought by haze with HQPs, the decoder equipped with a novel
normalized feature alignment module can effectively utilize high-quality
features and produce clean results. However, although our degradation pipeline
drastically mitigates the domain gap between synthetic and real data, it is
still intractable to avoid it, which challenges HQPs matching in the wild.
Thus, we re-calculate the distance when matching the features to the HQPs by a
controllable matching operation, which facilitates finding better counterparts.
We provide a recommendation to control the matching based on an explainable
solution. Users can also flexibly adjust the enhancement degree as per their
preference. Extensive experiments verify the effectiveness of our data
synthesis pipeline and the superior performance of RIDCP in real image
dehazing.Comment: Acceptted by CVPR 202
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, which leverages pretrained text-to-image diffusion models
for blind image restoration problem. Our framework adopts a two-stage pipeline.
In the first stage, we pretrain a restoration module across diversified
degradations to improve generalization capability in real-world scenarios. The
second stage leverages the generative ability of latent diffusion models, to
achieve realistic image restoration. Specifically, we introduce an injective
modulation sub-network -- LAControlNet for finetuning, while the pre-trained
Stable Diffusion is to maintain its generative ability. Finally, we introduce a
controllable module that allows users to balance quality and fidelity by
introducing the latent image guidance in the denoising process during
inference. Extensive experiments have demonstrated its superiority over
state-of-the-art approaches for both blind image super-resolution and blind
face restoration tasks on synthetic and real-world datasets. The code is
available at https://github.com/XPixelGroup/DiffBIR
Digital watermarking in medical images
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/12/2005.This thesis addresses authenticity and integrity of medical images using watermarking. Hospital Information Systems (HIS), Radiology Information Systems (RIS) and Picture Archiving and Communication Systems (P ACS) now form the information infrastructure for today's healthcare as these provide new ways to store, access and distribute medical data that also involve some security risk. Watermarking can be seen as an additional tool for security measures. As the medical tradition is very strict with the quality of biomedical images, the watermarking method must be reversible or if not, region of Interest (ROI) needs to be defined and left intact. Watermarking should also serve as an integrity control and should be able to authenticate the medical image. Three watermarking techniques were proposed. First, Strict Authentication Watermarking (SAW) embeds the digital signature of the image in the ROI and the image can be reverted back to its original value bit by bit if required. Second, Strict Authentication Watermarking with JPEG Compression (SAW-JPEG) uses the same principal as SAW, but is able to survive some degree of JPEG compression. Third, Authentication Watermarking with Tamper Detection and Recovery (AW-TDR) is able to localise tampering, whilst simultaneously reconstructing the original image
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Collecting paired training data is difficult in practice, but the unpaired
samples broadly exist. Current approaches aim at generating synthesized
training data from the unpaired samples by exploring the relationship between
the corrupted and clean data. This work proposes LUD-VAE, a deep generative
method to learn the joint probability density function from data sampled from
marginal distributions. Our approach is based on a carefully designed
probabilistic graphical model in which the clean and corrupted data domains are
conditionally independent. Using variational inference, we maximize the
evidence lower bound (ELBO) to estimate the joint probability density function.
Furthermore, we show that the ELBO is computable without paired samples under
the inference invariant assumption. This property provides the mathematical
rationale of our approach in the unpaired setting. Finally, we apply our method
to real-world image denoising and super-resolution tasks and train the models
using the synthetic data generated by the LUD-VAE. Experimental results
validate the advantages of our method over other learnable approaches
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