Single-Shot Retinal Image Enhancement Using Deep Image Priors

Qayyum, Adnan; Sultani, Waqas; Shamshad, Fahad; Qadir, Junaid; Tufail, Rashid

doi:10.1007/978-3-030-59722-1_61

Cited by 7 publications

(21 citation statements)

References 25 publications

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“…The key idea of the proposed method is image decomposition, where we decomposed the input degraded image into individual components and then we use the image formation (the enhanced image by DIP 1 ), 0 2 = t(x) (the transmission map by DIP 2 ), and o 2 = A the uniform atmospheric light (estimated using either DCP or BCP). In our previous work [22], we employed a separate DIP network for estimating non uniform A(x). To reconstruct the recovered image all individual components are mixed using image information model.…”

Section: Methodsmentioning

confidence: 99%

Single-Shot Retinal Image Enhancement Using Untrained and Pretrained Neural Networks Priors Integrated with Analytical Image Priors

Qayyum¹,

Sultani²,

Shamshad³

et al. 2021

Preprint

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Retinal images acquired using fundus cameras are often visually blurred due to imperfect imaging conditions, refractive medium turbidity, and motion blur. In addition, ocular diseases such as the presence of cataract also result in blurred retinal images. The presence of blur in retinal fundus images reduces the effectiveness of the diagnosis process of an expert ophthalmologist or a computer-aided detection/diagnosis system. In this paper, we put forward a single-shot deep image prior (DIP)-based approach for retinal image enhancement. Unlike typical deep learning-based approaches, our method does not require any training data. Instead, our DIP-based method can learn the underlying image prior while using a single degraded image. To perform retinal image enhancement, we frame it as a layer decomposition problem and investigate the use of two well-known analytical priors, i.e., dark channel prior (DCP) and bright channel prior (BCP) for atmospheric light estimation. We show that both the untrained neural networks and the pretrained neural networks can be used to generate an enhanced image while using only a single degraded image. We evaluate our proposed framework quantitatively on five datasets using three widely used metrics and complement that with a subjective qualitative assessment of the enhancement by two expert ophthalmologists. We have compared our method with a recent state-of-the-art method cofe-Net using synthetically degraded retinal fundus images and show that our method outperforms the state-of-the-art method and provides a gain of 1.23 and 1.4 in average PSNR and SSIM respectively. Our method also outperforms other works proposed in the literature, which have evaluated their performance on non-public proprietary datasets, on the basis of the reported results.

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Section: Methodsmentioning

confidence: 99%

Single-Shot Retinal Image Enhancement Using Untrained and Pretrained Neural Networks Priors Integrated with Analytical Image Priors

Qayyum¹,

Sultani²,

Shamshad³

et al. 2021

Preprint

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“…The development of these hand-crafted models with a focus on refining discriminative features and efficient optimization algorithms for a range of medical imaging problems has been the central research topic in the past [40], [41]. Successful hand-crafted models in medical imaging include total-variation [42], non-local self-similarity [43], sparsity/structured sparsity [44], Markov-tree models on wavelet coefficients [45], and untrained neural networks [46]- [48]. These models have been extensively leveraged in medical domain for image segmentation [49], reconstruction [50], disease classification [51], enhancement [52], and anomaly detection [53] due to their interpretability with solid mathematical foundations and theoretical supports on the robustness, recovery, and complexity [54], [55].…”

Section: Hand-crafted Approachesmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

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Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

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“…In [39], the authors demonstrated that coupled UNNP networks are capable of decomposing an input image into its individual elements, e.g., decomposing an image into foreground and background for a segmentation task. Qayyum et al [16] leveraged this idea of image decomposition using coupled UNNP networks. They proposed an unsupervised framework for retinal fundus image enhancement using coupled UNNP networks by integrating dark channel prior loss.…”

Section: Haze Removal In Fundus Imagingmentioning

confidence: 99%

“…More specifically, the deep network itself is used as the regularizer to the inverse problem. The authors showed that the architecture of the DNN is biased to natural images and is capable of capturing low-level image statistics without being explicitly trained using large scale [9], (c) from [15], (d) from [16], (e) from [17], and (f) from [18].…”

Section: Untrained Neural Network Priors: An Introductionmentioning

confidence: 99%