Evaluation of influences of frequency and amplitude on image degradation caused by satellite vibrations

Satellite vibrations during exposure will lead to pixel aliasing of remote sensors, resulting in the deterioration of image quality. In this paper, we expose the problem and discuss the characteristics of satellite vibrations, and then present a pixel mixing model. The idea of mean mixing ratio （MMR） is proposed. MMR computations for different frequencies are implemented. In the mixing model, a coefficient matrix is introduced to estimate each mixed pixel. Thus, the simulation of degraded image can be performed when the vibration attitudes are known. The computation of MMR takes into considera- tion the influences of various frequencies and amplitudes. Therefore, the roles of these parameters played in the degradation progress are identified. Computations show that under the same vibration amplitude, the influence of vibrations fluctuates with the variation of frequency. The fluctuation becomes smaller as the frequency rises. Two kinds of vibration imaging experiments are performed： different amplitudes with the same frequency and different frequencies with the same amplitude. Results are found to be in very good agreement with the theoretical results. MMR has a better description of image quality than modulation transfer function （MTF）. The influence of vibrations is determined mainly by the amplitude rather than the frequency. The influence of vibrations on image quality becomes gradually stable with the increase of frequency.

A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,images are often affected by various types of degradation which can significantly impact the performance of CNNs.In this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded images.The proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of degradation.The DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the image.We evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 database.The experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded images.It suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.

Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

:In recent years,video surveillance application played a significant role in our daily lives.Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility.The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and surveillance.To remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the past.However,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications.The existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric light.This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images.The proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct properties.The generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free image.Experiments are conducted on FRIDA dataset and haze images.To assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance metrics.Experimental results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so on.Experimental results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.

Novel Adaptive Binarization Method for Degraded Document Images

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast,bleedthrough,and nonuniform illumination effects.Unlike the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization.To enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced image.For the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic windows.The dynamic window is derived by exploiting the advantage of Otsu thresholding.To assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for experimentation.The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

Real-World Superresolution by Using Deep Degradation Learning

Most current deep convolutional neural networks can achieve excellent results on a single image superresolution and are trained using corresponding high-resolution(HR)images and low-resolution(LR)images.Conversely,their superresolution performance in real-world superresolution tests is reduced because thesemethods create paired LR images by simply interpolating and downsampling HR images,which is very different from natural degradation.In this article,we design a new unsupervised framework conditioned by degradation representations of real-world hyperresolution problems.The approach presented in this paper consists of three stages:we first learn the implicit degradation representation from real-world LR images and then acquire LR images by shrinking the network,which will share similar degradation with real-world images.Finally,we make paired data of the generated real LR images and HR images for training the SR network.Our approach can obtain better results than the recent SR approach on the NTIRE2020 real-world SR challenge Track1 dataset.

A pH-switched mesoporous nanoreactor for synergetic therapy

Zinc oxide nanoparticles （ZnO NPs）, as a new type of pH-sensitive drug carrier, have received much attention. ZnO NPs are stable at physiological pH, but can dissolve quickly in the acidic tumor environment （pH 〈 6） to generate cytotoxic zinc ions and reactive oxygen species （ROS）. However, the protein corona usually causes the non-specific degradation of ZnO NPs, which has limited their application considerably. Herein, a new type of pH-sensitive nanoreactor （ZnO-DOX@F-mSiO2-FA）, aimed at reducing the non-specific degradation of ZnO NPs, is presented. In the acidic tumor environment （pH 〈 6）, it can release cytotoxic zinc ions, ROS, and anticancer drugs to kill cancer cells effectively. In addition, the fluorescence emitted from fluorescein isothiocyanate （FITC）-labeled mesoporous silica （F-mSiO2） and doxorubicin （DOX） can be used to monitor the release behavior of the anticancer drug. This report provides a new method to avoid the non-specific degradation of ZnO NPs, resulting in synergetic therapy by taking advantage of ZnO NPs-induced oxidative stress and targeted drug release.