Single-Shot Retinal Image Enhancement Using Deep Image Priors

2020 ◽  
pp. 636-646
Author(s):  
Adnan Qayyum ◽  
Waqas Sultani ◽  
Fahad Shamshad ◽  
Junaid Qadir ◽  
Rashid Tufail
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Single Shot ◽  
Deep Image ◽  
Image Priors

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

2021 ◽  
Author(s):  
Adnan Qayyum ◽  
Waqas Sultani ◽  
Fahad Shamshad ◽  
Rashid Tufail ◽  
Junaid Qadir
Keyword(s):  
Neural Networks ◽  
Image Enhancement ◽  
Retinal Image ◽  
State Of The Art ◽  
Retinal Images ◽  
Single Shot ◽  
Fundus Images ◽  
Image Prior ◽  
Degraded Image ◽  
Retinal Fundus

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.

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

2021 ◽  
Author(s):  
Adnan Qayyum ◽  
Waqas Sultani ◽  
Fahad Shamshad ◽  
Rashid Tufail ◽  
Junaid Qadir
Keyword(s):  
Neural Networks ◽  
Image Enhancement ◽  
Retinal Image ◽  
State Of The Art ◽  
Retinal Images ◽  
Single Shot ◽  
Fundus Images ◽  
Image Prior ◽  
Degraded Image ◽  
Retinal Fundus

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.

scholarly journals Robust Retinal Image Enhancement via Dual-Tree Complex Wavelet Transform and Morphology-Based Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47303-47316 ◽  
Author(s):  
Dongming Li ◽  
Lijuan Zhang ◽  
Changming Sun ◽  
Tingting Yin ◽  
Chen Liu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Retinal Image ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

scholarly journals Retinal Fundus Image Enhancement Using the Normalized Convolution and Noise Removing

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peishan Dai ◽  
Hanwei Sheng ◽  
Jianmei Zhang ◽  
Ling Li ◽  
Jing Wu ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Median Filter ◽  
Basic Information ◽  
Fundus Image ◽  
Denoising Method ◽  
Convolution Algorithm ◽  
Retinal Fundus Image ◽  
Normalized Convolution ◽  
Retinal Fundus

Retinal fundus image plays an important role in the diagnosis of retinal related diseases. The detailed information of the retinal fundus image such as small vessels, microaneurysms, and exudates may be in low contrast, and retinal image enhancement usually gives help to analyze diseases related to retinal fundus image. Current image enhancement methods may lead to artificial boundaries, abrupt changes in color levels, and the loss of image detail. In order to avoid these side effects, a new retinal fundus image enhancement method is proposed. First, the original retinal fundus image was processed by the normalized convolution algorithm with a domain transform to obtain an image with the basic information of the background. Then, the image with the basic information of the background was fused with the original retinal fundus image to obtain an enhanced fundus image. Lastly, the fused image was denoised by a two-stage denoising method including the fourth order PDEs and the relaxed median filter. The retinal image databases, including the DRIVE database, the STARE database, and the DIARETDB1 database, were used to evaluate image enhancement effects. The results show that the method can enhance the retinal fundus image prominently. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.

scholarly journals A double-pass fundus reflection model for efficient single retinal image enhancement

2021 ◽  
pp. 108400
Author(s):  
Shuhe Zhang ◽  
Carroll A.B. Webers ◽  
Tos T.J.M. Berendschot
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Double Pass ◽  
Reflection Model
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