Generative and Discriminative Learning for Distorted Image Restoration
- URL: http://arxiv.org/abs/2011.05784v3
- Date: Fri, 27 Nov 2020 06:09:41 GMT
- Title: Generative and Discriminative Learning for Distorted Image Restoration
- Authors: Yi Gu, Yuting Gao, Jie Li, Chentao Wu, Weijia Jia
- Abstract summary: Liquify is a technique for image editing, which can be used for image distortion.
We propose a novel generative and discriminative learning method based on deep neural networks.
- Score: 22.230017059874445
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Liquify is a common technique for image editing, which can be used for image
distortion. Due to the uncertainty in the distortion variation, restoring
distorted images caused by liquify filter is a challenging task. To edit images
in an efficient way, distorted images are expected to be restored
automatically. This paper aims at the distorted image restoration, which is
characterized by seeking the appropriate warping and completion of a distorted
image. Existing methods focus on the hardware assistance or the geometric
principle to solve the specific regular deformation caused by natural
phenomena, but they cannot handle the irregularity and uncertainty of
artificial distortion in this task. To address this issue, we propose a novel
generative and discriminative learning method based on deep neural networks,
which can learn various reconstruction mappings and represent complex and
high-dimensional data. This method decomposes the task into a rectification
stage and a refinement stage. The first stage generative network predicts the
mapping from the distorted images to the rectified ones. The second stage
generative network then further optimizes the perceptual quality. Since there
is no available dataset or benchmark to explore this task, we create a
Distorted Face Dataset (DFD) by forward distortion mapping based on CelebA
dataset. Extensive experimental evaluation on the proposed benchmark and the
application demonstrates that our method is an effective way for distorted
image restoration.
Related papers
- PtychoDV: Vision Transformer-Based Deep Unrolling Network for
Ptychographic Image Reconstruction [12.780951605821238]
PtychoDV is a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction.
Results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem.
arXiv Detail & Related papers (2023-10-11T14:01:36Z) - Deformation-Invariant Neural Network and Its Applications in Distorted
Image Restoration and Analysis [8.009077765403287]
Images degraded by geometric distortions pose a significant challenge to imaging and computer vision tasks such as object recognition.
Deep learning-based imaging models usually fail to give accurate performance for geometrically distorted images.
We propose the deformation-invariant neural network (DINN), a framework to address the problem of imaging tasks for geometrically distorted images.
arXiv Detail & Related papers (2023-10-04T08:01:36Z) - All-in-one Multi-degradation Image Restoration Network via Hierarchical
Degradation Representation [47.00239809958627]
We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet)
AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering.
This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
arXiv Detail & Related papers (2023-08-06T04:51:41Z) - Zero shot framework for satellite image restoration [25.163783640750573]
We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration.
Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics.
arXiv Detail & Related papers (2023-06-05T14:34:58Z) - Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
We present a learning-based solution for restoring images suffering from spatially-varying degradations.
We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
arXiv Detail & Related papers (2021-08-19T11:02:25Z) - A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency.
We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution.
Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
arXiv Detail & Related papers (2020-07-21T10:03:42Z) - Learning to Restore a Single Face Image Degraded by Atmospheric
Turbulence using CNNs [93.72048616001064]
Images captured under such condition suffer from a combination of geometric deformation and space varying blur.
We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image.
arXiv Detail & Related papers (2020-07-16T15:25:08Z) - Invertible Image Rescaling [118.2653765756915]
We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images.
We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
arXiv Detail & Related papers (2020-05-12T09:55:53Z) - Exploiting Deep Generative Prior for Versatile Image Restoration and
Manipulation [181.08127307338654]
This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images.
The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
arXiv Detail & Related papers (2020-03-30T17:45:07Z) - Self-Supervised Linear Motion Deblurring [112.75317069916579]
Deep convolutional neural networks are state-of-the-art for image deblurring.
We present a differentiable reblur model for self-supervised motion deblurring.
Our experiments demonstrate that self-supervised single image deblurring is really feasible.
arXiv Detail & Related papers (2020-02-10T20:15:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.