ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application
- URL: http://arxiv.org/abs/2407.08498v2
- Date: Sun, 21 Jul 2024 03:03:12 GMT
- Title: ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application
- Authors: Liang Wu, Wenjing Lu, Liming Tang, Zhuang Fang,
- Abstract summary: The Retinex theory models the image as a segmentation of illumination and noise components.
We propose an exponential decomposition algorithm for image denoising.
- Score: 3.9304843171575112
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Retinex theory models the image as a product of illumination and reflection components, which has received extensive attention and is widely used in image enhancement, segmentation and color restoration. However, it has been rarely used in additive noise removal due to the inclusion of both multiplication and addition operations in the Retinex noisy image modeling. In this paper, we propose an exponential Retinex decomposition model based on hybrid non-convex regularization and weak space oscillation-modeling for image denoising. The proposed model utilizes non-convex first-order total variation (TV) and non-convex second-order TV to regularize the reflection component and the illumination component, respectively, and employs weak $H^{-1}$ norm to measure the residual component. By utilizing different regularizers, the proposed model effectively decomposes the image into reflection, illumination, and noise components. An alternating direction multipliers method (ADMM) combined with the Majorize-Minimization (MM) algorithm is developed to solve the proposed model. Furthermore, we provide a detailed proof of the convergence property of the algorithm. Numerical experiments validate both the proposed model and algorithm. Compared with several state-of-the-art denoising models, the proposed model exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM).
Related papers
- DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement [73.57965762285075]
We propose a new expression called Digital-Imaging Retinex theory (DI-Retinex) through theoretical and experimental analysis of Retinex theory in digital imaging.
Our proposed method outperforms all existing unsupervised methods in terms of visual quality, model size, and speed.
arXiv Detail & Related papers (2024-04-04T09:53:00Z) - Robust image segmentation model based on binary level set [3.6985338895569204]
This paper models the illumination term in intensity inhomogeneity images.
To enhance the model's robustness to noisy images, we incorporate the binary level set model into the proposed model.
By introducing the variational operator GL, our model demonstrates better capability in segmenting noisy images.
arXiv Detail & Related papers (2024-03-20T08:33:40Z) - A locally statistical active contour model for SAR image segmentation
can be solved by denoising algorithms [6.965119490863576]
Experimental results for real SAR images show that the proposed image segmentation model can efficiently stop the contours at weak or blurred edges.
The proposed FPRD1/FPRD2 models are about 1/2 (or less than) of the time required for the SBRD model based on the Split Bregman technique.
arXiv Detail & Related papers (2024-01-10T00:27:14Z) - A Novel Truncated Norm Regularization Method for Multi-channel Color
Image Denoising [5.624787484101139]
This paper is proposed to denoise color images with a double-weighted truncated nuclear norm minus truncated Frobenius norm minimization (DtNFM) method.
Through exploiting the nonlocal self-similarity of the noisy image, the similar structures are gathered and a series of similar patch matrices are constructed.
Experiments on synthetic and real noise datasets demonstrate that the proposed method outperforms many state-of-the-art color image denoising methods.
arXiv Detail & Related papers (2023-07-16T03:40:35Z) - 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) - Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
CT Reconstruction [108.06731611196291]
We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies.
We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
arXiv Detail & Related papers (2022-03-10T14:22:54Z) - Designing a Practical Degradation Model for Deep Blind Image
Super-Resolution [134.9023380383406]
Single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
This paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations.
arXiv Detail & Related papers (2021-03-25T17:40:53Z) - Regularization by Denoising Sub-sampled Newton Method for Spectral CT
Multi-Material Decomposition [78.37855832568569]
We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT.
In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function.
We show numerical and experimental results for spectral CT materials decomposition.
arXiv Detail & Related papers (2021-03-25T15:20:10Z) - 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) - Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector
Tensor Factorization [42.56231647066719]
Hyperspectral image (HSI) has some advantages over natural image for various applications due to the extra spectral information.
During the acquisition, it is often contaminated by severe noises including Gaussian noise, impulse noise, deadlines, and stripes.
We present a HSI restoration method named smooth and robust low rank tensor recovery.
arXiv Detail & Related papers (2020-06-29T02:10:07Z) - A Set-Theoretic Study of the Relationships of Image Models and Priors
for Restoration Problems [34.956580494340166]
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.
arXiv Detail & Related papers (2020-03-29T09:33:47Z)
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.