Blind restoration of astronomical image based on deep attention generative adversarial neural network

The use of neural network pattern recognition techniques in the field of astronomy is reviewed. In assessing the quality of image recognition derived from this method particular attention is given to the problem of star/galaxy discrimination in large digital sky surveys. A two color survey of 9 fields of the first epoch Palomar Sky Survey, centered on the North Galactic Pole, has been performed with the Minnesota Automated Plate Scanner. A set of neural network image classifiers are used to automatically perform star/galaxy discrimination. We assess the efficiency of image classification and sample completeness through comparisons with a variety of independent studies of the NGP area.

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A Blur Classification Approach Using Deep Convolution Neural Network

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2020010106 ◽

2020 ◽

Vol 11 (1) ◽

pp. 93-111

Author(s):

Shamik Tiwari

Keyword(s):

Neural Network ◽

Convolution Neural Network ◽

Hand Gesture ◽

Classification Approach ◽

Degraded Image ◽

Blind Restoration ◽

Deep Convolution Neural Network ◽

Simulation Results ◽

Mlp Model ◽

Degraded Images

Computer vision-based gesture identification is designed to recognize human actions with the help of images. During the process of gesture image acquisition, images suffer various degradations. The method of recovering these degraded images is called restoration. In the case of blind restoration of such a degraded image where blur information is unavailable, it is essential to determine the exact blur type. This article presents a convolution neural network model for blur classification which categories a blur found in a hand gesture image into one of the four blur categories: motion, defocus, Gaussian, and box blur. The simulation results demonstrate the improved preciseness of the CNN model when compared to the MLP model.

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A Blind Restoration Approach for Defocused Barcode Images

International Journal of Strategic Information Technology and Applications ◽

10.4018/ijsita.2017070103 ◽

2017 ◽

Vol 8 (3) ◽

pp. 41-58

Author(s):

Shamik Tiwari

Keyword(s):

Neural Network ◽

Image Restoration ◽

Radial Basis Function ◽

Basis Function ◽

Accurate Estimation ◽

Robust Method ◽

Ridgelet Transform ◽

Accurate Representation ◽

Blurred Image ◽

Blind Restoration

Use of a mobile camera for barcode decoding provides high portability and availability but it requires that the recorded barcode image must be accurate representation of the barcode that is available on the product. Barcode scanning is challenging because images may be degraded due to out-of-focus blur at the time of image acquisition. Therefore, image restoration is essential in making image sharp and useful. In case of blind restoration of such barcode images accurate estimation of out-of-focus blur parameter is highly desirable. In this article, a robust method has been proposed for estimating the radius of out-of-focus blur. Finite discrete ridgelet transform has been used to find the features of the blurred image and a radial basis function neural network is utilized to estimate the radius of out-of-focus blur. The experimental results reveal that proposed method more robust than the existing methods.

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Space targets adaptive optics images blind restoration by convolutional neural network

Optical Engineering ◽

10.1117/1.oe.58.9.093102 ◽

2019 ◽

Vol 58 (09) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Jianglin Shi ◽

Rongzhi Zhang ◽

Shiping Guo ◽

Yikang Yang ◽

Rong Xu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Adaptive Optics ◽

Blind Restoration

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Construction of blind restoration model for super-resolution image based on chaotic neural network

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2019.109498 ◽

2020 ◽

Vol 131 ◽

pp. 109498

Author(s):

Congshi Jiang ◽

Quan Chen

Keyword(s):

Neural Network ◽

Super Resolution ◽

Chaotic Neural Network ◽

Resolution Image ◽

Blind Restoration ◽

Restoration Model

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BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

Iraqi Journal of Information & Communications Technology ◽

10.31987/ijict.1.1.178 ◽

2021 ◽

Vol 1 (1) ◽

pp. 25-32

Author(s):

Meryem H. Muhson ◽

Ayad A. Al-Ani

Keyword(s):

Neural Network ◽

Image Restoration ◽

Super Resolution ◽

Degraded Image ◽

Blind Restoration ◽

High Resolution Images ◽

Image Super Resolution ◽

Single Image Super Resolution ◽

Blurred Images ◽

Degraded Images

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.

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