Fast Restoration for Out-of-Focus Blurred Images of QR Code With Edge Prior Information via Image Sensing

Rong-jun, Chen; Zheng, Zhijun; Yu, Yongxing; Zhao, Huimin; Ren, Jinchang; Tan, Hongzhou

doi:10.1109/jsen.2021.3085568

Cited by 21 publications

(11 citation statements)

References 55 publications

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“…Blur regions can be discovered using a coarse blur detection step. [6] Chen suggested the Quick Restoration technique to restore the clarity of QR code images that have blurred because they are out of focus. This technique makes advantage of edge prior information, which is knowledge about the typical separation between the centre and edges of clear QR code images.…”

Section: Literature Surveymentioning

confidence: 99%

Reconstruction of Blur Images for Quality Enhancement

Sriteja¹,

Bhavana²,

Sandya³

et al. 2023

Int. J. Res. Publ. Rev.

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Blurred images are a widespread issue in a variety of industries, including agriculture, construction, vintage photographs, CCTV film, and military imagery.Motions made by portable devices or other objects. In these circumstances, it is essential to precisely evaluate the image quality and blur profile. To improve blurry photos, a variety of techniques, including different algorithms, have been suggested. The Generative Adversarial Networks (GAN) approach, which can complete a variety of reconstruction tasks utilising just single-source data as input, is one of the most efficient and effective algorithms for this task. Superresolution reconstruction (SRR) techniques, which turn low-resolution images into high-resolution ones, frequently employ GANs. With built-in preprocessing, GANs have a generator that generates images based on input data and a discriminator that compares and determines whether created images are authentic or not.SRGAN is a GAN extension that offers superior outcomes than the original GAN. SRGAN provides several benefits when used for blur picture reconstruction and quality improvement. In a recent study, an ImageNet dataset was used to train and test GAN and a hybrid model that combined SRResNet and SRGAN. The SSIM and the PSNR metric are typically used in image de-blurring to determine the perceived image quality. The outcomes demonstrated that the suggested hybrid model outperformed GAN.

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Section: Literature Surveymentioning

confidence: 99%

Reconstruction of Blur Images for Quality Enhancement

Sriteja¹,

Bhavana²,

Sandya³

et al. 2023

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

show abstract

“…According to a comprehensive review by Wudhikarn et al [1], oriented object detection approaches based on deep learning have been explored for barcode detection in the last few years [4,5]. In terms of barcode image deblurring, Shi et al [6] provide an excellent review of state-of-the-art deblurring approaches, which are generally based on traditional image restoration techniques with focus on utilization of image statistics as priors (e.g., image distribution and edge information) to estimate the blur kernel (i.e., transformation) and restore the sharp image through an iterative process [6,7]. According to a survey by Zhang et al [8], a variety of architectures have been explored for deblurring in other domains, including deep autoencoders (DAE), cascaded networks, multi-scale networks, GANs, etc.…”

Section: Related Workmentioning

confidence: 99%

Using Deep Learning to Improve Detection and Decoding Of Barcodes

Wang

Guevara²,

Caragea

2022

2022 IEEE International Conference on Image Processing (ICIP)

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We propose an end-to-end pipeline for transforming raw images containing barcodes into sharp barcode images that can be accurately decoded. Our pipeline leverages recent deep learning approaches and consists of a rotation-decoupled detector (RDD) for oriented barcode detection and a deblurring model. The deblurring model uses a generative adversarial network (specifically, DeblurGAN-v2) trained on pairs of noisy and sharp images generated using ground truth numeric codes. Evaluation of the proposed pipeline using real barcode images enhanced by the DeblurGAN-v2 model shows a 14% improvement of the decoding rate as compared to the decoding rate obtained on the original images.

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“…Thus, research attention has shifted towards image deblurring methods. Blurred images not only subjectively affect the visual experience 2 – 4 , but also affect subsequent visual tasks 5 . Accordingly, addressing image deblurring techniques for dynamic scenes emerges as a crucial problem to solve 6 .…”

Section: Introductionmentioning

confidence: 99%

An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

Lian,

Wang

2023

Sci Rep

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The investigation of image deblurring techniques in dynamic scenes represents a prominent area of research. Recently, deep learning technology has gained extensive traction within the field of image deblurring methodologies. However, such methods often suffer from limited inherent interconnections across various hierarchical levels, resulting in inadequate receptive fields and suboptimal deblurring outcomes. In U-Net, a more adaptable approach is employed, integrating diverse levels of features effectively. Such design not only significantly reduces the number of parameters but also maintains an acceptable accuracy range. Based on such advantages, an improved U-Net model for enhancing the image deblurring effect was proposed in the present study. Firstly, the model structure was designed, incorporating two key components: the MLFF (multilayer feature fusion) module and the DMRFAB (dense multi-receptive field attention block). The aim of these modules is to improve the feature extraction ability. The MLFF module facilitates the integration of feature information across various layers, while the DMRFAB module, enriched with an attention mechanism, extracts crucial and intricate image details, thereby enhancing the overall information extraction process. Finally, in combination with fast Fourier transform, the FRLF (Frequency Reconstruction Loss Function) was proposed. The FRLF obtains the frequency value of the image by reducing the frequency difference. The present experiment results reveal that the proposed method exhibited higher-quality visual effects. Specifically, for the GoPro dataset, the PSNR (peak signal-to-noise ratio) reached 31.53, while the SSIM (structural similarity index) attained a value of 0.948. Additionally, for the Real Blur dataset, the PSNR achieved 31.32, accompanied by an SSIM score of 0.934.

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Fast Restoration for Out-of-Focus Blurred Images of QR Code With Edge Prior Information via Image Sensing

Abstract: Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing.

Cited by 21 publications

References 55 publications

Reconstruction of Blur Images for Quality Enhancement

Reconstruction of Blur Images for Quality Enhancement

Using Deep Learning to Improve Detection and Decoding Of Barcodes

An image deblurring method using improved U-Net model based on multilayer fusion and attention mechanism

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