A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems

• URL: http://arxiv.org/abs/2003.12985v1
• Date: Sun, 29 Mar 2020 09:33:47 GMT
• Title: A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems
• Authors: Bihan Wen, Yanjun Li, Yuqi Li, and Yoram Bresler
• Abstract summary: We study how effective each image model is for image restoration. We compare the denoising results which are consistent with our analysis. On top of the model-based methods, we quantitatively demonstrate the image properties that are inexplicitly exploited by deep learning method.
• Score: 34.956580494340166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image prior modeling is the key issue in image recovery, computational imaging, compresses sensing, and other inverse problems. Recent algorithms combining multiple effective priors such as the sparse or low-rank models, have demonstrated superior performance in various applications. However, the relationships among the popular image models are unclear, and no theory in general is available to demonstrate their connections. In this paper, we present a theoretical analysis on the image models, to bridge the gap between applications and image prior understanding, including sparsity, group-wise sparsity, joint sparsity, and low-rankness, etc. We systematically study how effective each image model is for image restoration. Furthermore, we relate the denoising performance improvement by combining multiple models, to the image model relationships. Extensive experiments are conducted to compare the denoising results which are consistent with our analysis. On top of the model-based methods, we quantitatively demonstrate the image properties that are inexplicitly exploited by deep learning method, of which can further boost the denoising performance by combining with its complementary image models.

Related papers

• Deep Learning CT Image Restoration using System Blur and Noise Models [2.2530496464901106]
  We present a method that leverages both degraded image inputs and a characterization of the system blur and noise to combine modeling and deep learning approaches. Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics.
  arXiv  Detail & Related papers  (2024-07-20T21:17:35Z)
• ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application [3.9304843171575112]
  The Retinex theory models the image as a segmentation of illumination and noise components. We propose an exponential decomposition algorithm for image denoising.
  arXiv  Detail & Related papers  (2024-07-11T13:34:37Z)
• Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey [96.99328714941657]
  We present a comprehensive review of recent diffusion model-based methods on image restoration. We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR. We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models [8.122270502556374]
  We present a novel patch-based image restoration algorithm based on denoising diffusion probabilistic models. We demonstrate our approach to achieve state-of-the-art performances on both weather-specific and multi-weather image restoration.
  arXiv  Detail & Related papers  (2022-07-29T11:52:41Z)
• A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory [40.29747436872773]
  This paper presents a novel image denoising algorithm inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics. We show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications.
  arXiv  Detail & Related papers  (2021-12-16T23:34:37Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [55.28436972267793]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Learning Integrodifferential Models for Image Denoising [14.404339094377319]
  We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. By accumulating weighted structural information on multiple scales, our model is the first to create anisotropy through multiscale integration.
  arXiv  Detail & Related papers  (2020-10-21T10:50:29Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Depth image denoising using nuclear norm and learning graph model [107.51199787840066]
  Group-based image restoration methods are more effective in gathering the similarity among patches. For each patch, we find and group the most similar patches within a searching window. The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
  arXiv  Detail & Related papers  (2020-08-09T15:12:16Z)

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.