Document Image Binarization Using Dual Discriminator Generative Adversarial Networks

De, Rajonya; Chakraborty, Anuran; Sarkar, Ram

doi:10.1109/lsp.2020.3003828

Cited by 37 publications

(23 citation statements)

References 21 publications

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“…Vo et al [22] designed a CNN-based end-to-end binarization method by using multi-scale deep supervised networks. Also, after the introduction of generative models, image binarization systems with generative models have been suggested [23]. For enhancing the performance of image binarization, Bhunia et al [24] and Zhao et al [25] develop binarization systems using a conditional GAN (cGAN).…”

Section: A Image Binarization and Background Blurring Methodsmentioning

confidence: 99%

Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

2021

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Image-to-image conversion tasks are more accurate and sophisticated than ever thanks to advances in deep learning. However, since typical deep learning models are trained to perform only one task, multiple trained models are required to perform each task even if they are related to each other. For example, the popular image-to-image convolutional neural network, U-Net, is normally trained for a single task. Based on U-Net, this study proposes a model that outputs variable results using only one trained model. The proposed method produces a continuously changing output by setting an external parameter. We confirm the robustness of our proposed model by evaluating it on binarization and background blurring. According to these evaluations, we confirmed that the proposed model can generate well-predicted outputs by using un-trained tuning parameters as well as the outputs by using trained tuning parameters. Furthermore, the proposed model can generate extrapolated outputs outside the learning range.INDEX TERMS Image-to-image conversion, multiple tasks, U-Net, image binarization, background blur

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Section: A Image Binarization and Background Blurring Methodsmentioning

confidence: 99%

Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

2021

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“…Image-to-image translation [ 34 ] is an important application of GANs. This has been applied to many tasks, such as super-resolution [ 35 ], binarization [ 36 , 37 ], document analysis [ 38 , 39 ], etc. In addition to the above, many CNN based approaches have also been proposed for COVID-19 detection in radiological images.…”

Section: Related Workmentioning

confidence: 99%

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Karbhari¹,

Basu

Geem

et al. 2021

Diagnostics

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COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

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“…Unlike in the classic Sauvola 's threshold estimation function (5), SauvolaNet is end-to-end trainable and doesn't require any hyper-parameter. Similar to Eq.…”

Section: Related Sauvola Approachesmentioning

confidence: 99%

“…Recent efforts [2,19,9,34,5,30,32] focus more on improving robustness and generalizability. In particular, the SAE approach [2] suggests estimating the pixel memberships not from a DNN's raw output but the DNN's activation map, and thus generalizes well even for out-of-domain samples with a weak activation map.…”

Section: Introductionmentioning

confidence: 99%

“…The DeepOtsu method [9] learns a DNN that iteratively enhances a degraded image to a uniform image and then binarized via the classic Otsu approach. Finally, generative adversarial networks (GAN) based approaches like cGANs [34] and DD-GAN [5] rely on the adversarial training to improve the model's robustness against local noises by penalizing those problematic local pixel locations that the discriminator uses in differentiating real and fake results.…”

Section: Introductionmentioning

confidence: 99%

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SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

Deng¹,

Wu²,

Zhou³

2021

Preprint

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Inspired by the classic Sauvola local image thresholding approach, we systematically study it from the deep neural network (DNN) perspective and propose a new solution called SauvolaNet for degraded document binarization (DDB). It is composed of three explainable modules, namely, Multi-Window Sauvola (MWS), Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold (AST). The MWS module honestly reflects the classic Sauvola but with trainable parameters and multi-window settings. The PWA module estimates the preferred window sizes for each pixel location. The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location. As a result, SauvolaNet becomes end-to-end trainable and significantly reduces the number of required network parameters to 40K -it is only 1% of MobileNetV2. In the meantime, it achieves the State-of-The-Art (SoTA) performance for the DDB task -SauvolaNet is at least comparable to, if not better than, SoTA binarization solutions in our extensive studies on the 13 public document binarization datasets. Our source code is available at https://github.com/Leedeng/SauvolaNet.

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Document Image Binarization Using Dual Discriminator Generative Adversarial Networks

Cited by 37 publications

References 21 publications

Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

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