scholarly journals BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

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.  

scholarly journals Hyperspectral Image Super-Resolution with 1D–2D Attentional Convolutional Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2859 ◽  
Author(s):  
Jiaojiao Li ◽  
Ruxing Cui ◽  
Bo Li ◽  
Rui Song ◽  
Yunsong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Small Change ◽  
Single Image Super Resolution ◽  
Separation Strategy

Hyperspectral image (HSI) super-resolution (SR) is of great application value and has attracted broad attention. The hyperspectral single image super-resolution (HSISR) task is correspondingly difficult in SR due to the unavailability of auxiliary high resolution images. To tackle this challenging task, different from the existing learning-based HSISR algorithms, in this paper we propose a novel framework, i.e., a 1D–2D attentional convolutional neural network, which employs a separation strategy to extract the spatial–spectral information and then fuse them gradually. More specifically, our network consists of two streams: a spatial one and a spectral one. The spectral one is mainly composed of the 1D convolution to encode a small change in the spectrum, while the 2D convolution, cooperating with the attention mechanism, is used in the spatial pathway to encode spatial information. Furthermore, a novel hierarchical side connection strategy is proposed for effectively fusing spectral and spatial information. Compared with the typical 3D convolutional neural network (CNN), the 1D–2D CNN is easier to train with less parameters. More importantly, our proposed framework can not only present a perfect solution for the HSISR problem, but also explore the potential in hyperspectral pansharpening. The experiments over widely used benchmarks on SISR and hyperspectral pansharpening demonstrate that the proposed method could outperform other state-of-the-art methods, both in visual quality and quantity measurements.

A Blur Classification Approach Using Deep Convolution Neural Network

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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