Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network

Liu, Haofeng; Li, Heng; Fu, Huazhu; Xiao, Ruoxiu; Gao, Yunshu; Hu, Yan; Liu, Jiang

doi:10.1007/978-3-031-16434-7_49

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…Moreover, [27] introduced an unpaired image-to-image translation method for converting low-quality images into their high-quality counterparts. Similarly, Liu et al (2022) [28] proposed the pyramid constraint to create a degradation-invariant supervised learning enhancement network (PCE-Net). This approach reduces the need for clinical data and effectively enhances the hidden intrinsic dataset.…”

Section: Introductionmentioning

confidence: 99%

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

Likassa,

Chen,

Chen

et al. 2024

J. Imaging

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Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,*) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method’s superiority over existing state-of-the-art methods across various datasets.

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Section: Introductionmentioning

confidence: 99%

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

Likassa,

Chen,

Chen

et al. 2024

J. Imaging

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“…Recently, researchers have proposed numerous deep learning-based algorithms for image enhancement and vessel segmentation, aiming to alleviate the burden on doctors [ 5 , 6 ]. Hu et al proposed a novel SuperVessel algorithm that takes low-resolution images as input and produces high-resolution and accurate vessel segmentation [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model

Liu,

Huang

2023

Biomed. Opt. Express

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In clinical screening, accurate diagnosis of various diseases relies on the extraction of blood vessels from fundus images. However, clinical fundus images often suffer from uneven illumination, blur, and artifacts caused by equipment or environmental factors. In this paper, we propose a unified framework called ESDiff to address these challenges by integrating retinal image enhancement and vessel segmentation. 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. Furthermore, we utilize 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 and pertinent information. This approach enhances the intermediate results within the iterative process of the diffusion model. Extensive experiments on publicly available fundus retinal datasets (i.e. DRIVE, STARE, CHASE_DB1 and EyeQ) demonstrate the effectiveness of ESDiff compared to state-of-the-art methods.

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“…Shen et al develop a degradation framework based on the imaging principles of fundus cameras, which is then used to design a correction network named CofeNet [23]. Built on this same degradation pipeline, Liu et al propose the Pyramid Constraint Network (PCENet) to enhance clinically-relevant representation [16]. Li et al present the Structure-Consistent Restoration Network (SCRNet) for cataract fundus images, which lays its foundation on the consistency of high-frequency components [13].…”

Section: Introductionmentioning

confidence: 99%

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

Cheng¹,

Li²,

Huang³

et al. 2023

Preprint

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The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from unpaired high-quality to low-quality images. We then apply a conditional diffusion model to learn the inverse enhancement process in a paired manner. The proposed LED is able to output enhancement results that maintain clinically important features with better clarity. Moreover, in the inference phase, LED can be easily and effectively integrated with any existing fundus image enhancement framework. We evaluate the proposed LED on several downstream tasks with respect to various clinically-relevant metrics, successfully demonstrating its superiority over existing state-of-the-art methods both quantitatively and qualitatively. The source code is available at https://github.com/QtacierP/LED.

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Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network

Cited by 13 publications

References 21 publications

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement

ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

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