Complex Wavelet Structural Similarity: A New Image Similarity Index

2009 ◽  
Vol 18 (11) ◽  
pp. 2385-2401 ◽  
Author(s):  
M.P. Sampat ◽  
Zhou Wang ◽  
S. Gupta ◽  
A.C. Bovik ◽  
M.K. Markey
Keyword(s):  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Complex Wavelet

scholarly journals Dual tree complex wavelet transform based image denoising for Kalpana satellite images

2018 ◽  
Vol 7 (3.29) ◽  
pp. 269
Author(s):  
Naga Lingamaiah Kurva ◽  
S Varadarajan
Keyword(s):  
Wavelet Transform ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Pictorial Representation ◽  
Complex Wavelet Transform ◽  
Raw Data ◽  
Complex Wavelet ◽  
Kalpana Satellite

This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.   

scholarly journals Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1462 ◽  
Author(s):  
Jinhua Liu ◽  
Yunbo Rao ◽  
Yuanyuan Huang
Keyword(s):  
Image Watermarking ◽  
Similarity Index ◽  
Low Frequency ◽  
Visual Saliency ◽  
Structural Similarity ◽  
High Entropy ◽  
Saliency Model ◽  
Watermark Embedding ◽  
Complex Wavelet ◽  
Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.

scholarly journals Modify Manhattan Distance for Image Similarity

2020 ◽  
Vol 2 (4) ◽  
pp. 12-16
Author(s):  
Tasaddi Maalak Hanoun ◽  
Kadhim M. Hashim
Keyword(s):  
Statistical Approach ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Manhattan Distance ◽  
The Novel ◽  
Structural Similarity Index ◽  
Significant Difference ◽  
Index Measure ◽  
Similarity Method

A New measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (lowPSNR). The proposed measure, denoted by Manhattan distance and STD, uses a combination of two parts: the first part is the Geometric method, while the second part is based on the statistical feature. The concept of manhattan distance is used in the geometric part. The new measure shows the advantages of statistical approaches and geometric approaches. The proposed similarity method is an outcome for the human face. The novel measure outperforms the classical SSIM in detecting image similarity at low PSNR, with a significant difference in performance. AMS subject classification:

scholarly journals Lifting dual tree complex wavelets transform

2021 ◽  
Vol 11 (5) ◽  
pp. 4008
Author(s):  
Hilal Naimi ◽  
Amelbahahouda Adamou-Mitiche ◽  
Lahcène Mitiche
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Computation Time ◽  
Structural Similarity ◽  
Complex Wavelet Transform ◽  
Complex Wavelets ◽  
Wavelets Transform ◽  
Complex Wavelet ◽  
Segmentation Image

We describe the lifting dual tree complex wavelet transform (LDTCWT), a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). We describe a way to estimate the accuracy of this approximation and style appropriate filters to attain this. These benefits are often exploited among applications like denoising, segmentation, image fusion and compression. The results of applications shrinkage denoising demonstrate objective and subjective enhancements over the dual tree complex wavelet transform (DTCWT). The results of the shrinkage denoising example application indicate empirical and subjective enhancements over the DTCWT. The new transform with the DTCWT provide a trade-off between denoising computational competence of performance, and memory necessities. We tend to use the PSNR (peak signal to noise ratio) alongside the structural similarity index measure (SSIM) and the SSIM map to estimate denoised image quality.

scholarly journals Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

2021 ◽  
Vol 12 (6) ◽  
pp. 871-877
Author(s):  
Min-Jeong Kim Et.al
Keyword(s):  
Image Processing ◽  
Global Warming ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Structural Similarity Index ◽  
Pavement Deterioration ◽  
Region Extraction ◽  
Index Measure ◽  
Scale Classification

Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

scholarly journals GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1269
Author(s):  
Jiabin Luo ◽  
Wentai Lei ◽  
Feifei Hou ◽  
Chenghao Wang ◽  
Qiang Ren ◽  
...  
Keyword(s):  
Image Denoising ◽  
Data Augmentation ◽  
Noise Suppression ◽  
Random Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Multi Scale ◽  
Convolutional Autoencoder

Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

scholarly journals Microwave Imaging of Breast Skin Utilizing Elliptical UWB Antenna and Reverse Problems Algorithm

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 647
Author(s):  
Sameer Alani ◽  
Zahriladha Zakaria ◽  
Tale Saeidi ◽  
Asmala Ahmad ◽  
Muhammad Ali Imran ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Imaging System ◽  
Signal Transmission ◽  
Similarity Index ◽  
Structural Similarity ◽  
Microwave Imaging ◽  
Ultra Wideband ◽  
Uwb Antenna ◽  
System A ◽  
High Structural Similarity

Skin cancer is one of the most widespread and fast growing of all kinds of cancer since it affects the human body easily due to exposure to the Sun’s rays. Microwave imaging has shown better outcomes with higher resolution, faster processing time, mobility, and less cutter and artifact effects. A miniaturized elliptical ultra-wideband (UWB) antenna and its semi-spherical array arrangement were used for signal transmission and reception from the defected locations in the breast skin. Several conditions such as various arrays of three, six, and nine antenna elements, smaller tumor, multi-tumors, and skin on a larger breast sample of 30 cm were considered. To assess the ability of the system, a breast shape container with a diameter of 130 mm and height of 60 mm was 3D printed and then filled with fabricated skin and breast fat to perform the experimental investigation. An improved modified time-reversal algorithm (IMTR) was used to recreate 2D images of tumors with the smallest radius of 1.75 mm in any location within the breast skin. The reconstructed images using both simulated and experimental data verified that the system can be a reliable imaging system for skin cancer diagnosis having a high structural similarity index and resolution.

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