A Comprehensive Survey on DigitalI image Denoising Methods Using Statistical Models in the Transform Domain with the Comparison of Them
Mansoore
Saeedzarandi
Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar, University of Kerman, Kerman, Iran
author
Hossein
Nezamabadi-pour
Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar, University of Kerman, Kerman, Iran
author
Saeid
Saryazdi
Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar, University of Kerman, Kerman, Iran
author
Ahad
Jamalizadeh
Department of Statistics, Faculty of Mathematics & Computer, Shahid Bahonar University of Kerman,
Kerman, Iran
author
text
article
2021
per
Image denoising is a well explored topic. Generally, image denoising approaches can be categorized as spatial domain and transform domain methods according to the image representation. Transform domain methods can be divided into two main groups according to their basis functions. Transform domain methods with data adaptive basis functions and transform domain methods with fixed basis functions. Fixed basis functions transform methods, in which, wavelet transform is the most popular, have been widely used for noise reduction applications due to their features and properties, such as frequency / space separation. Also, due to the non-static nature of natural images and the addition of noise to them, statistical methods have received a lot of attention among transform methods. In the present paper, after a brief introduction of denoising methods, the most important statistical models in the fixed basis transform domain are studied. The experimental results are discussed and analyzed to determine the advantages and disadvantages of these methods. The comprehensive study in this paper is a good reference for new research ideas in image denoising.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
1
23
https://jmvip.sinaweb.net/article_118217_e5b6deace5d8ff2096cbfbd744230964.pdf
Stain Normalization of Histopathology Images using conditional Generative Adversarial Networks (cGAN)
Pegah
Salehi
Image Processing Research Lab
Dept of Computer Eng. & Info. Tech.
RAZI University, Kermanshah, IRAN
author
Abdolah
Chalechale
Image Processing Research Lab
Dept of Computer Eng. & Info. Tech.
RAZI University, Kermanshah, IRAN
author
text
article
2021
per
The diagnosis of cancer is mainly performed by visual analysis of pathologists through examining the morphology of the tissue slices under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and without texture. Therefore, chemical staining is required to create adequate contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, and types of illness, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for stain normalization have been proposed as a pre-processing step in the pipeline of the automated systems. The pix2pix methodwhich is derived from the conditional Generative Adversarial Networks (cGAN) is one of the powerful methods for solving image-to-image translation problems. The main innovation of this paper is to present a new powerful method for the stain normalization of histopathology images using the Pix2Pix method, which is implemented and evaluated on the Mitos-Atypia-14 dataset.In the proposed method, grayscale images are given as input to the network, and then the system learns to restain the texture of the input image in a specific coloring style by preserving the structure and corresponding histopathological pattern. This method, compared to previous methods that relied on a reference image, instead uses the distribution of all images in the learning phase. The proposed method has achieved significant resultsboth in quantitative and qualitative evaluations comparing to some well-known methods in the literature.Moreover, as another innovation, the proposed method tested in a clinical use-case, namely breast cancer tumor classification,using the PatchCamelyon datasetand itshowsa 5% increase in the AUC parameter.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
25
41
https://jmvip.sinaweb.net/article_118500_eb46ae4e784bf61fdac04116f9a622d7.pdf
Improving the Accuracy of Natural Dynamic Scenes Recognition using Correlation of Feature Maps in CNNs
Safoora
Heidari
Phd. Student of Electrical Engineering, Ferdowsi University of Mashhad (FUM), Mashhad, Iran
author
Abbas
Ebrahimi moghadam
Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University Of Mashhad (FUM), Mashhad, Iran
author
Morteza
Khademi Doroh
Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University Of Mashhad (FUM), Mashhad, Iran
author
Hadi
Hadizadeh
Department of Electrical Engineering, Quchan University of Technology
author
text
article
2021
per
Dynamic scene recognition is one of the fundamental research fields in machine vision. In this paper, an effective dynamic scene recognition method using convolutional neural networks is proposed. In the proposed method the correlation of feature maps of different layers in a neural network is exploited as a feature vector containing video information. Firstly, N frames of video are selected and fed into a network to exploit the feature maps, then a Gram matrix indicating the spatial information of the frames of video is calculated. Subsequently, using temporal slicing over selected frames and averaging over the Gram matrices of these frames, temporal information is considered. Encoding features followed by pooling operation, a feature vector is obtained for classification. Experimental evaluations on benchmark dynamic scene datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods in this research field and has improved the recognition accuracy about 9% for Maryland dataset and about 3% for YUP++ dataset.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
43
55
https://jmvip.sinaweb.net/article_118502_5ce4c76c8035c5bd2eeba18aa146787b.pdf
New Grayscale Image Encryption Based on Advanced Encryption Standard and DNA Sequence
Amirhossein
Razmi
Msc. Student of Computer Engineering, Islamic Azad University Central Tehran Branch, Tehran, Iran
author
Kooroush
Manochehri
Computer Engineering Department,, Amirkabir University of Technology (Garmsar Campus), Tehran, Iran
author
Alireza
Hedayati
Department of Computer Engineering, Islamic Azad University of Central Tehran Branch, Tehran, Iran
author
text
article
2021
per
An image is a visual representation of something that has been created or copied and stored in electronic form. Securing images is becoming an important concern in today’s information security due to the extensive use of images that are either transmitted over a network or stored on disks. Since public media are unreliable and vulnerable to attacks, Image encryption is the most effective way to fulfil confidentiality and protect the privacy of images over an unreliable public media.In this paper a new image encryption algorithm based on Advanced Encryption Standard and DNA sequence is proposed. We present how to encode and decode data in a DNA sequence based on Codon replacement and how to perform the different steps of AES based DNA. The algorithm is implemented in MATLAB 2012b and various performance metrics are used to evaluate its efficacy. The theoretical and experimental analysis show that the proposed algorithm is efficient in speed and precision. Furthermore, the security analysis proves that proposed algorithm has a good resistance against the noise and known attacks; So that Unbreakability of proposed algorithm is 37.48% better than the compared algorithms.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
57
71
https://jmvip.sinaweb.net/article_118508_31cf6f1f0fd9116d52b63528d7a91b48.pdf
Classification of retrieved images based on SIFT and Locality Constrained Linear Coding
Mohsen
Jaberi
MsC of Electrical Engineering, Semnan University
author
Farzin
Yaghmaee
Dep. of Electrical and Computer Engineering, Semnan University
author
text
article
2021
per
With the growing Internet and digital imaging tools, the size of the image database is increasing rapidly. Therefore, there is a strong need for tools and methods to search for images in a large database. Feature extraction is the most basic step in creating an image-retrieval systems. This paper presents a new method for image retrieval systems. After extracting the feature and computing descriptors for each category by the SIFT algorithm, then the appropriate descriptors are identified by the TF-IDF algorithm and used clustering to find candidate descriptors for each category. In the next step, the descriptor coefficients of each category were used with regard to the representatives from the previous stage by the local coding algorithm as the attribute. Finally we used Extreme Learning Machine (ELM) for classification. Experimental results show that the accuracy achieved in proposed method on the Caltech-101 database is about 98.5% and in Flowers data set is about 97.9%.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
73
84
https://jmvip.sinaweb.net/article_120264_c5e563f6f9480aeb708998935254621e.pdf
Blood Vessels Extraction from MRA Images by a Region Growing Algorithm Based on a New Nonlinear Contrast Stretching Function and Shearlets Frame
Mehdi
Mirzafam
PhD Student, Azarbaijan Shahid Madani University, Tabriz
author
Nasser
Aghazadeh
Image Processing Laboratory, Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran
author
text
article
2021
per
Region growing, in a simple version, is a segmentation process, which having pixels as seeds, pixels with the same intensities and connected them are added to the area gradually, and finally presents a binary image that contains the object or objects of the target. So far, many binary segmentation techniques have been developed to extract target objects, with the common disadvantage that they do not perform the extraction task completely. Frames as the generalization of orthogonal bases are used scarcely in these algorithms. In this paper, a new nonlinear contrast stretching function is introduced, and then, based on the contrast stretching function andshearlets frame, correct initialization seeds are extractedand then the region growing algorithm applyto the image. The results presented on synthetic images and real medical images show the advantages of our technique to those recently proposed.
Journal of Machine Vision and Image Processing
Iranian Society of Machine Vision and Image Processing
2383-1197
8
v.
2
no.
2021
85
99
https://jmvip.sinaweb.net/article_122739_f1506bf0647585566b77de25abfd1c5f.pdf