Blind image deblurring using class-adapted image priors

Ljubenovic, Marina; Figueiredo, Mário A. T.

doi:10.1109/icip.2017.8296329

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…Ljubenovic et al proposed a method that estimates the original images and the motion blur using minimum mean squared error (MMSE) estimators, which are based on the alternating direction method of multipliers (ADMM) and a Gaussian mixture model (GMM). They verified the performance of the method using fingerprint images [19]. Lopez et al attempted motion deblurring using an ensemble neural network and fuzzy methods rather than using an estimation function.…”

Section: Related Workmentioning

confidence: 96%

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

et al. 2020

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Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user's finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods. INDEX TERMS Biometrics, finger wrinkle recognition, generative adversarial network (GAN), restoration of motion blurred image.

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Section: Related Workmentioning

confidence: 96%

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

et al. 2020

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“…Teodoro et al propose using a Gaussian mixture model as a class-specific patch-based prior, for solving two image inverse problems, namely non-blind deblurring and compressive imaging [45]. In prior work, we have shown that class-specific GMM-based image prior can be used for BID [27]. That approach allows handling situations where the image being processed contains regions of different classes, as done by Teodoro et al for denoising and non-blind deblurring [44].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…In particular, we use denoisers based on Gaussian mixture models (GMM) or dictionaries, learned from patches of clean images from specific classes. These types of denoisers can be used for images that contain only one class, as presented in [27], or two or more classes [28].…”

Section: Introductionmentioning

confidence: 99%

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Plug-and-play approach to class-adapted blind image deblurring

Ljubenovic

Figueiredo

2019

IJDAR

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Most of the existing single-image blind deblurring methods are tailored for natural images. However, in many important applications (e.g., document analysis, forensics), the image being recovered belongs to a specific class (e.g., text, faces, fingerprints) or contains two or more classes. To deal with these images, we propose a class-adapted blind deblurring framework, based on the plug-and-play scheme, which allows forward models of imaging systems to be combined with state-of-the-art denoisers. We consider three patch-based denoisers, two suitable for images that belong to a specific class and a general purpose one. Additionally, for images with two or more classes, we propose two approaches: a direct one, and one that uses a patch classification step before denoising. The proposed deblurring framework includes two priors on the blurring filter: a sparsity-inducing prior, suitable for motion blur and a weak prior, for a variety of filters. The results show the state-of-the-art performance of the proposed framework when applied to images that belong to a specific class (text, face, fingerprints), or contain two classes (text and face). For images with two classes, we show that the deblurring performance is improved by using the classification step. For these images, we choose to test one instance of the proposed framework suitable for text and faces, which is a natural test ground for the proposed framework. With the proper (dictionary and/or classifier) learning procedure, the framework can be adapted to other problems. For text images, we show that, in most cases, the proposed deblurring framework improves OCR accuracy. Keywords Blind deblurring • Class-specific image priors • Plug-and-play • ADMM • Patch-based image processing The research leading to these results has received funding

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“…The closest class-specific approach to our work is the one proposed in [26], [27], [28], [29]. It shows improvement in image restoration tasks (denoising and deblurring) by adapting the reconstruction algorithm to a specific class of images.…”

Section: Related Workmentioning

confidence: 99%

Class-Aware Fully Convolutional Gaussian and Poisson Denoising

Remez

Litany

Giryes

et al. 2018

IEEE Trans. on Image Process.

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We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances the state-of-the-art when trained for different noise levels and distributions (both Gaussian and Poisson). In addition, we show that making the denoiser class-aware by exploiting semantic class information boosts performance, enhances textures and reduces artifacts.

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Blind image deblurring using class-adapted image priors

Cited by 15 publications

References 29 publications

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

Plug-and-play approach to class-adapted blind image deblurring

Class-Aware Fully Convolutional Gaussian and Poisson Denoising

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