Diffusion model image enhancement. party/sykior/regedit-auto-headshot-vip-apk-download.

These models also show exceptional performance in enhancing underwater images. This paper studies a diffusion-based framework to address Mar 3, 2024 · This paper introduces a novel UIE framework, named PA-Diff, designed to exploiting the knowledge of physics to guide the diffusion process, and proves that this method achieves best performance on UIE tasks. By leveraging the Multi-Domain Learning (MDL) paradigm, our proposed model is endowed with Dec 20, 2023 · Abstract: Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Recently, diffusion models were employed to underwater image Multi-Domain Multi-Scale Diffusion Model for Low-Light Image Enhancement (AAAI'24) - Oliiveralien/MDMS {Multi-Domain Multi-Scale Diffusion Model for Low-Light CPDM: Content-Preserving Diffusion Model for Underwater Image Enhancement Xiaowen Shi and Yuan-Gen Wang School of Computer Science and Cyber Engineering, Guangzhou University, China shixiaowen@e. Megvii Technology, Abstract. g. Latent Diffusion Model for Medical Image Standardization and Enhancement Md Selim, Jie Zhang, Faraneh Fathi, Michael A. Second, to leverage the learned degradation representations, we develop a Low-Light Diffusion model (LLDiffusion) with a well-designed dynamic diffusion module. DiffWater: Underwater Image Enhancement Based on Conditional Denoising Diffusion Probabilistic Model. SU-DDPM outperforms other baseline and generative adversarial network models in underwater image Explore a collection of engaging articles on Zhihu's column, covering diverse topics from introspection to astronomy. edu. Pyramid-based Diffusion Model, a type of generative model capable of modeling and generating high-dimensional data distributions, have recently been explored for low-light image enhancement. To better account for non-Gaussian noise in the real Dec 5, 2023 · This work introduces a novel diffusion model-based framework for image enhancement, incorporating mask refinement as an auxiliary task via a image enhancement and vessel mask-aware diffusion model, and utilizes low-quality retinal fundus images and their corresponding illumination maps as inputs to the modified UNet to obtain degradation factors that effectively preserve pathological features 2. UnderwaterImage Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy. Guo X Li Y Ling H Lime: low-light image enhancement via illumination map estimation IEEE Trans. In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. This model can represent various image restoration tasks. Thanks! author={Guan, Meisheng and Xu, Haiyong and Jiang, Gangyi Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. Aug 18, 2023 · In this paper, we are the first to present a comprehensive review of recent diffusion model-based methods on image restoration, encompassing the learning paradigm, conditional strategy, framework design, modeling strategy, and evaluation. Underwater visuals undergo various complex degradations, inevitably influencing the efficiency of underwater vision tasks. To Jun 28, 2023 · This is the code repo of our ICIP2023 work that proposes a novel approach to low-light image enhancement using the diffusion model (LLDE). Limited generalization capability has been an unsolved issue in the domain of low-light image enhancement. To combat these image degradations, post This paper presents L 2 DM, a novel framework for low-light image enhancement using diffusion models. To this end, First, a joint learning framework for both image generation and image enhancement is presented to learn the degradation representations. Enhancement process. 2023. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from unpaired high-quality to low-quality images. Underwater image enhancement (UIE) is challenging since image degradation in DOI: 10. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. The code of paper "Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement" - chenydong/PA-Diff Feb 22, 2024 · This paper propose the Patch-based Simplified Conditional Diffusion Model (PSC Diffusion) for low-light image enhancement due to the outstanding performance of diffusion models in image generation. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that Nov 10, 2023 · In BDCE, a bootstrap diffusion model is presented for model the distribution of optimal curve parameters, which can then be used for high resolution images. May 25, 2024 · Additionally, for image enhancement tasks, we observe that both the image-to-image diffusion model and CLIP-Classifier primarily focus on the high-frequency region during fine-tuning. The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. The training of the diffusion models contains both of the processes, while only the denoising process is conducted in the inference stage. This paper presents L2DM, a novel framework for low-light image enhancement using diffusion mod-els. Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. 1. , et al. Since L 2 DM falls into the category of latent diffusion models, it can reduce computational requirements through denoising and the diffusion process in latent space. 2) The proposed framework eliminates the requirement of paired data. Therefore, we propose a new fine-tuning strategy that specifically targets the high-frequency region, which can be up to 10 times faster than traditional strategies. Expand. Requirements A suitable conda environment can be created and activated with: 2. DiffusionCT incorporates an Unet-based encoder-decoder Aug 18, 2022 · Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance. Specifically, LLDiffusion mainly contains a latent map encoder, a degradation generation network (DGNET), and a dy-namic degradation-aware diffusion module (DDDM). 2 Image Enhancement with Denoising Algorithm In general, image enhancement tasks can be formulated by: y = Hx+n, (3) where y is the degraded image, H is a degradation matrix, x is the unknown original image, and n is the independent random noise. We observed extremely noisy training patterns Oct 27, 2023 · Wan F Xu B Pan W Liu H (2024) PSC diffusion: patch-based simplified conditional diffusion model for low-light image enhancement Multimedia Systems 10. 2016 26 2 982 993 3604838 10. Jul 7, 2024 · Abstract. cn, wangyg@gzhu. May 21, 2024 · Therefore, low-light image enhancement is a crucial yet challenging problem in computer vision, aiming to recover high-quality images. Hai, J. To be specific, inspired by the recent research that low curvature ODE-trajectory results in a stable and effective diffusion process, we Oct 26, 2023 · A curvature regularization term anchored in the intrinsic non-local structures of image data is formulated, i. 14501 (2021) Google Oct 1, 2023 · Current underwater image enhancement methods have poor generalization capability and cannot be adapted to all types of underwater images. In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier. Dec 20, 2023 · Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. License BSD-3-Clause license Mar 24, 2024 · Diffusion models have achieved remarkable progress in low-light image enhancement. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Recalling the conditional DDPM model discussed in Sec. The enhancement process basically follows the traditional decomposition model, thus the reflectance is firstly separated from the intensity channel of the given image as follows: (7) T (x) = I (x) L (x) + ξ, where ξ is the small positive number to avoid the problem of zero division. , denoising and super-resolution) mainly rely on paired data and correspondingly the well-trained models can only handle one type of task. Therefore, we propose a fast fine-tuning strategy focusing on the high-frequency region, which can be up to 10 times faster than the traditional strategy. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes in imaging conditions or training instability. Furthermore, we utilize low-quality retinal fundus images and their corresponding illumination maps as inputs to the modified UNet to obtain degradation factors [IJCAI 2023 ORAL] "Pyramid Diffusion Models For Low-light Image Enhancement" (Official Implementation) - limuloo/PyDIff Underwater image enhancement, Diffusion model, Non-uniform sampling ACM Reference Format: YiTang,HiroshiKawasaki,andTakafumiIwaguchi. Concretely, we first introduce the background of the diffusion model briefly and then present two prevalent . The model can gradually generate high-quality fundus images, and it improves the quality of images and the accuracy of blood vessel segmentation by refining the mask. Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various forms of degradation. Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT). : R2RNet: low-light image enhancement via real-low to real-normal network. Mar 16, 2023 · Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. Brooks, Ge Wang, Guoqiang Yu, and Jin Chen Abstract—Computed tomography (CT) serves as an effective tool for lung cancer screening, diagnosis, treatment, and prog-nosis, providing a rich source of features to quantify temporal Aug 18, 2023 · This paper is the first to present a comprehensive review of recent diffusion model-based methods on image restoration, encompassing the learning paradigm, conditional strategy, framework design, modeling strategy, and evaluation, and presents two prevalent workflows that exploit diffusion models in image restoration. 1, the enhancement task can be performed due to the superiority of the generative model conditioned by low-light images [34, 14]. Many models find enhancing out-of-distribution Jul 27, 2023 · Current deep learning methods for low-light image enhancement (LLIE) typically rely on pixel-wise mapping learned from paired data. Mar 8, 2023 · The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. Diffusion model-based text-guided enhancement network for medical image segmentation @article{Dong2024DiffusionMT, title={Diffusion model-based text-guided enhancement network for medical image segmentation}, author={Zhiwei Dong and Genji Yuan and Zhen Hua and Jinjiang Li}, journal={Expert Syst. Inspired by the DDPM, an underwater image A dataset of the Beidou Navigation Satellite for on-orbit low-light image enhancement (LLIE) and a novel diffusion model to enhance the image contrast without over-exposure and blurring details, which indicates that the method has a better capacity in on-orbit LLIE. Diffusion Posterior Sampling for General Noisy Inverse Problems, Hyungjin Chung et al. 1007/s00530-024-01391-z 30:4 Online publication date: 21-Jun-2024 T, and images with longer exposure time are gradually achieved. Existing underwater image enhancement (UIE) methods often lack generalization capabilities, making them unable to adapt to various underwater images captured in different aquatic environments and lighting conditions. arXiv preprint arXiv:2106. This paper introduces a dual Dec 12, 2023 · Distinguishing itself from previous image enhancement methods that rely on conditional diffusion models , our proposed approach utilizes a vessel mask-aware diffusion model. 1. Nov 29, 2023 · Specifically, we introduce a novel diffusion model-based framework for image enhancement, incorporating mask refinement as an auxiliary task via a vessel mask-aware diffusion model. Jul 6, 2024 · Resolution enhancement with a diffusion model. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. In this paper, we propose a new approach that utilizes the powerful generative network, the deep diffusion model, to regard LIE as a task of generating normal Denoising/Enhancement: OCT: image: 18: PET Image Denoising with Score-Based Diffusion Probabilistic Models link: Denoising/Enhancement: PET: image: 19: DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution pdf: Super-resolution: link: MRI: image: 20: InverseSR: 3D Brain MRI Super-Resolution Using a Latent Dec 28, 2023 · This paper presents L 2 DM, a novel framework for low-light image enhancement using diffusion models. Sep 26, 2023 · Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. Conditioning inputs are essential for guiding the enhancement process diffusion model for image enhancement. Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model, Yinhuai Wang et al. 3. 1007/978-981-99-8552-4_11 Corpus ID: 266965685; L2DM: A Diffusion Model for Low-Light Image Enhancement @inproceedings{Lv2023L2DMAD, title={L2DM: A Diffusion Model for Low-Light Image Enhancement}, author={Xingguo Lv and Xingbo Dong and Zhe Jin and Hui Zhang and Siyi Song and Xuejun Li}, booktitle={Chinese Conference on Pattern Recognition and Computer Vision}, year={2023}, url={https @article{cheng2023learning, title={Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement}, author={Cheng, Pujin and Lin, Li and Huang, Yijin and He, Huaqing and Luo, Wenhan and Tang, Xiaoying}, journal={arXiv preprint arXiv:2303. Mathematical restoration models, in particular, total variation-based models can easily lose fine structures during image denoising. Hai Jiang 1,2 , Ao Luo 2 , Haoqiang Fan 2 , Songchen Han 1 , Shuaicheng Liu 3,2 1. Image Process. Conditioning inputs are essential for guiding the enhancement process, therefore applies the diffusion model with Retinex model for low-light image enhancement. In this paper, we address the limitation with a diffusion model Aug 22, 2020 · Abstract. Since L2DM falls into the category of latent diffusion models, it can reduce computational requirements through denoising and the diffusion process in latent space. 1) An unsupervised plug-and-play framework is constructed by integrating diffusion model and general image restoration. In this article, we propose a data-driven method for low-light image enhancement (LLIE) of spin targets in space environment based on diffusion model. 2639450 1409. Related Work 2. Firstly, by analyzing the change of information entropy in TOFD image, a segmentation method of defect region and background region based on information entropy is proposed. Please list the main weaknesses of the paper. Specifically, we first adopt a data-driven Paper Info Reviews Meta-review Author Feedback Post-Rebuttal Meta-reviews Authors Jun Ma, Yuanzhi Zhu, Chenyu You, Bo Wang Abstract Deep learning-based medical image enhancement methods (e. Diffusion Models for Image Restoration and Enhancement – A Comprehensive Survey. 1 Jun 2023 · Hai Jiang , Ao Luo , Songchen Han , Haoqiang Fan , Shuaicheng Liu ·. Diffusion models (DMs) is a new generative model based on deep learning with computational vision, which has been applied to image generation, image enhancement, image restoration, text-to-image and other fields. SU-DDPM outperforms other baseline and generative adversarial network models in underwater image Jun 21, 2024 · The complex iterative diffusion steps of the diffusion model enable the generation of images with richer details, inspiring its application in the task of low-light image enhancement. However, the noise and artifacts in low-light images inevitably mislead the diffusion model and cause detail loss and unsatisfactory color. Fei et al. 2016. , ICLRW 2022 | Code Jul 25, 2023 · Image enhancement is one of the bases of image processing technology, which can enhance useful features and suppress useless information of images according to the specified task. However, these methods often overlook the importance of considering degradation representations, which can lead to sub-optimal outcomes. In particular, we introduce a spatial-frequency fusion module to seamlessly integrates spatial and frequency information. [ paper] This Repo includes the training and testing codes of our DiffWater. Sichuan University, 2. Denoising Diffusion Restoration Models, Bahjat Kawar et al. However, these methods fail to consider the physical properties and underwater imaging mechanisms in the diffusion process, limiting information completion capacity of diffusion models. We aim to integrate the advantages of the physical model and the generative network. 1109/TIP. Oct 1, 2023 · However, extending the diffusion model developed for 2D photographic images to PET enhancement still faces two problems: a) three-dimensionsal (3D) PET images will dramatically increase the computational cost of diffusion model; b) PET is the detail-sensitive images and may be introduced/lost some details during the procedure of adding/removing Jan 28, 2024 · This article makes the first attempt to adapt the diffusion model to the UIE task and proposes a Content-Preserving Diffusion Model (CPDM), which first leverages a diffusion model as its fundamental model for stable training and then designs a content-preserving framework to deal with changes in imaging conditions. Previous work mainly treated LIE as a lighting enhancement work based on the Retinex theory. cn Abstract Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated Dec 5, 2023 · Abstract. 3) The pre-trained diffusion model can simultaneously deal with multiple tasks of medical image enhancement. 2. For instance, in the image denoising Apr 22, 2024 · Diffusion models have been increasingly utilized in various image-processing tasks, such as segmentation, denoising, and enhancement. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from Feb 28, 2007 · A NON-CONVEX DIFFUSION MODEL FOR SIMULTANEOUS IMAGE DENOISING AND EDGE ENHANCEMENT SEONGJAI KIM, HYEONA LIM Abstract. 2, “Methods” section), was trained to diminish the noise in well-aligned raw images. To address these limitations in one go, we propose a Multi-Domain Multi-Scale (MDMS) diffusion model for low-light image enhancement. [ 17 ] propose a unified diffusion prior method named GDP for various image restoration tasks including low-light image enhancement. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. To harness the capabilities of diffusion models, we delve into this intricate process and advocate for the regularization of its inherent ODE-trajectory. 04603}, year={2023} } Jul 27, 2023 · To this end, First, a joint learning framework for both image generation and image enhancement is presented to learn the degradation representations. Mar 3, 2024 · Recently, diffusion models were employed to underwater image enhancement (UIE) tasks, and gained SOTA performance. This section begins with a review of the evolutionary history of traditional Convolutional Neural Networks (CNNs) in the field of medical image segmentation, outlines the application of feature enhancement modules in deep learning, describes in detail the development of diffusion models, and, finally, summarizes the data challenges faced by medical image segmentation. Underwater images often suffer from serious color bias and blurred features because of the effect of the water bodies on the light. , 2021), which was the first introduction of the denoising diffusion model in the field of SE tasks. Recently, the explicit model-ing of the conditional distribution of normally-exposed im-ages is explored in [41], showing superior May 25, 2024 · A novel underwater image enhancement method, by utilizing the multi-guided diffusion model for iterative enhancement, which combines the prior knowledge from the in-air natural domain with Contrastive Language-Image Pretraining (CLIP) to train a classifier for controlling the diffusion model generation process. Nov 1, 2021 · 3. In Proceedings of the 31st ACM International Latent Diffusion Model for Medical Image Standardization and Enhancement. Edit social preview. The basic theory of diffusion models includes diffusion and denoising processes, as shown in Fig. In this study, we propose ReCo-Diff, a novel approach that incorporates Retinex-based prior as an additional pre-processing condition to regulate the generating capabilities of the diffusion model. In order to overcome the drawback, this article introduces two strategies: the non- for image enhancement tasks, we find that the image-to-image diffusion model and the CLIP-Classifier mainly act in the high-frequency region during the fine-tuning process. The diffusion model is applied to guide the multi-path adjustments of illumi-nation and reflectance maps for better performance. , 2022) is based on the prior diffusion probabilistic speech enhancement model (DiffuSE) (Lu et al. Retinex-based LLIE Methods The theory of the retinal-cortex (Retinex) is based on the model of color invariance and Aug 25, 2023 · In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. In order to ensure coherent enhancement for images with oriented flow-like structures, we propose a nonlinear diffusion system model based on time-fractional delay. Mar 8, 2023 · In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Low-Light Image Enhancement with Wavelet-based Diffusion Models. Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. low-light image enhancement. However, there remain two practical limitations: (1) existing methods mainly focus on the spatial domain for the diffusion process, while neglecting the essential features in the frequency domain; (2) conventional patch-based sampling strategy inevitably leads to severe checkerboard artifacts due to the uneven Apr 15, 2024 · This work presents a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity and takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. The image reconstruction losses L tof different steps are all used for training, and F θ(X 0) is the final result. To address these problems, we propose BDCE, a bootstrap diffusion model Dec 5, 2023 · Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. , arXiv 2022 | code. A Zhihu column that offers insights and discussions on various topics, such as relationships, self-improvement, and life experiences. To enhance underwater images, we present SU-DDPM, a method of real-time underwater image enhancement (UIE) based on a denoising diffusion probabilistic model (DDPM). In this article, we make the first attempt to adapt the diffusion model to the UIE Abstract. Low-light Image Enhancement (LIE) has received significant attention in the field of computer vision low-level tasks in recent years. Jan 20, 2023 · This work addresses the issue of CT image harmonization using a new diffusion-based model, named DiffusionCT, to standardize CT images acquired from different vendors and protocols. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative Jun 25, 2023 · Recently, deep learning approaches have achieved remarkable success in image enhancement of natural images datasets, but seldom applied in space due to the data bottleneck. DGNET is designed to learn the degradation process from normal-light images to low-light ones. We then apply a conditional diffusion model to learn Jun 1, 2024 · Finally, a diffusion model, namely UDiM (Supplementary Fig. In this paper, we address this limitation by proposing a degradation-aware learning scheme for LLIE using diffusion models Dec 19, 2023 · Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality, such as color distortion, reduced contrast, and noise. (Pytorch Version) If you use our code, please cite our paper and hit the star at the top-right corner. Jan 28, 2024 · Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. DiffusionCT is presented, an innovative score-based DDPM model that operates in the latent space to transform disparate non-standard distributions into a standardized form, providing a more consistent basis for downstream analysis of CT images. The DDDM is proposed to achieve image enhancement. Nonlinear diffusion equations have been successfully used for image enhancement by reducing the noise in the image while protecting the edges. By combining the nonlinear isotropic diffusion Oct 8, 2023 · The proposed model (LLDE) can outperform recent SOTAs quantitatively and visually and adopt a diffusion model for low-light image enhancement, as its way of encoding the mapping between the source and target distributions fits the idea. However, conventional models for underwater image enhancement often face the challenge of simultaneously improving color restoration and super-resolution. [Siggraph Asia 2023]Low-light Image Enhancement with Wavelet-based Diffusion Models . Additionally, for image enhancement tasks, we observe that both the image-to-image diffusion model and CLIP-Classifier primarily focus on the high-frequency region during fine-tuning. ReCo-Diff first leverages a pre-trained decomposition network to produce initial In order to overcome the above problems, an anisotropic diffusion model (P-M) based on region adaptive strategy is proposed to realize TOFD image enhancement. Furthermore, we hope to supplement and even deduce the Jan 28, 2024 · Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. Related work. In discretized form, the denoising requires the solution of a sequence of linear systems. Thanks! author={Guan, Meisheng and Xu, Haiyong and Jiang, Gangyi To the best of our knowledge, this is the first utilization of a diffusion model to simultaneously achieve low-quality image enhancement and fundus vessel segmentation. Extensive experiments on publicly available fundus retinal datasets demonstrate the effectiveness of ESDiff compared to state-of-the-art methods. gzhu. }, year={2024}, volume={249}, pages={123549 Mar 5, 2024 · A novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED, which utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains and successfully alleviates the dependence on pairwise training data via zero-reference learning. e. 94202 Google Scholar Digital Library; 17. Appl. Image restoration (IR) has been an indispensable and challenging task in the Oct 26, 2023 · This paper studies a diffusion-based framework to address the low-light image enhancement problem. We set a denoising module into each iteration of curve adjustment for enhancement and denoising simultaneously. The underlying system matrices stem from a discrete diffusion operator with large jumps in the Mar 26, 2024 · The conditional diffusion probabilistic model for speech enhancement (CDiffuSE) (Lu et al. DiffusionCT operates in the latent space by mapping a latent non-standard distribution into a standard one. 2. Abstract. Furthermore, each particular LLIE approach may introduce a different form of flaw within its enhanced results. , global structure-aware regularization, which gradually facilitates the preservation of complicated details and the augmentation of contrast during the diffusion process, leading to a more precise and flexible enhancement. In recent years, the diffusion model based on the denoising diffusion probabilistic model (DDPM) has achieved excellent results in various fields of computer vision. Compared with traditional autoregressive models, variational autoencoders (VAEs), energy-based models (EBMs), flow-based models (FBMs) and generative adversarial network (GAN) models Mar 8, 2023 · In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering. sx np xv bd kd dg ku ii vg ka