Representing Noisy Image Without Denoising
- URL: http://arxiv.org/abs/2301.07409v3
- Date: Wed, 19 Jun 2024 06:54:15 GMT
- Title: Representing Noisy Image Without Denoising
- Authors: Shuren Qi, Yushu Zhang, Chao Wang, Tao Xiang, Xiaochun Cao, Yong Xiang,
- Abstract summary: Fractional-order Moments in Radon space (FMR) is designed to derive robust representation directly from noisy images.
Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases.
- Score: 91.73819173191076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and an image security application are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.
Related papers
- Robust Representation Consistency Model via Contrastive Denoising [83.47584074390842]
randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations.
diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples.
We reformulate the generative modeling task along the diffusion trajectories in pixel space as a discriminative task in the latent space.
arXiv Detail & Related papers (2025-01-22T18:52:06Z) - VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference [5.852077003870417]
We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
We show that our VIPaint method significantly outperforms previous approaches in both the plausibility and diversity of imputations.
arXiv Detail & Related papers (2024-11-28T05:35:36Z) - Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
Supervised deep learning has become the method of choice for image denoising.
We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
arXiv Detail & Related papers (2024-07-24T16:23:46Z) - Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer [17.430622649002427]
Existing learning-based denoising methods typically train models to generalize the image prior from large-scale datasets.
We propose a new perspective on the denoising challenge by highlighting the distinct separation between noise and image priors.
We introduce a Locally Noise Prior Estimation algorithm, which accurately estimates the noise prior directly from a single raw noisy image.
arXiv Detail & Related papers (2024-07-12T08:43:11Z) - Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation.
Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping.
We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
arXiv Detail & Related papers (2023-06-23T18:08:00Z) - Masked Image Training for Generalizable Deep Image Denoising [53.03126421917465]
We present a novel approach to enhance the generalization performance of denoising networks.
Our method involves masking random pixels of the input image and reconstructing the missing information during training.
Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.
arXiv Detail & Related papers (2023-03-23T09:33:44Z) - Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image [34.27748767631027]
We present a novel self-supervised learning method for single-image denoising.
We approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network.
Our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM.
arXiv Detail & Related papers (2022-06-04T00:08:58Z) - Reconstructing the Noise Manifold for Image Denoising [56.562855317536396]
We introduce the idea of a cGAN which explicitly leverages structure in the image noise space.
By learning directly a low dimensional manifold of the image noise, the generator promotes the removal from the noisy image only that information which spans this manifold.
Based on our experiments, our model substantially outperforms existing state-of-the-art architectures.
arXiv Detail & Related papers (2020-02-11T00:31:31Z) - Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
Blind image denoising is an important yet very challenging problem in computer vision.
We propose a new variational inference method, which integrates both noise estimation and image denoising.
arXiv Detail & Related papers (2019-08-29T15:54:06Z)
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.