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Two Dimensional Discrete Wavelet Transform. The high and low pass filters operate separable on the rows and columns to create four different subbands. An 8x8 image is used for example purposes only.
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In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is
any wavelet transform for which the wavelets are discretely sampled. In this paper, there are given fundamental of DWT and implementation in MATLAB. Image is filtered by low pass(for smooth variation between gray level pixels) and high pass filter (for high variati...
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Citations
... Transforming a signal is actually just another way of representing that signal. Wavelet Transform provides time-frequency representation of the signal [37]. The transformation does not change the information contained in the signal; it just expands the signal into a wavelet domain. ...
... Since the image is a two-dimensional signal [39], a two-dimensional wavelet transform is applied. The two-dimensional wavelet transform is a one-dimensional analysis of a two-dimensional signal [37]. The wavelet chosen to perform the transformation in this paper is the Haar wavelet. ...
With the widespread use of the Internet and the decrease in storage costs, many media have been transferred to digital media. This situation reduces the security and reliability of digital media. Media producers use watermarking methods for copyright protection. This study focuses on wavelet transform, which is one of the frequency conversion methods for watermarking. The wavelet transform for the watermarking process is usually applied on one of the four subbands obtained in a single level. In this study, the watermarking process is carried out in a total of 12 sub-bands, including the 1st, 2nd and 3rd levels. In addition, a performance evaluation metric for digital image watermarking is presented. The evaluation is applied using 10 different watermark strength factors on 46 different image resolutions. The Ultra High-definition Demoiréing Dataset is used for testing. The mathematical results obtained as a result of 22080 iterations are shown with tables and graphics, and the performance relations between the cover image resolution value and the sub-band selection are interpreted.
... A DWT is a wavelet transform that decomposes the host signal into wavelets that are discontinuous (Begum vd., 2022). Two-dimensional wavelet transform is a one-dimensional analysis of a two-dimensional signal (Mistry, 2013). The image signal is passed through the Low Pass Filter (LPF) to obtain its important components. ...
Numerous methods have been proposed for recognizing characters in an image. Some of these use the Canny, Sobel and Prewitt edge detection algorithms. Images captured from the real world in applications such as license plate recognition contain a lot of noise due to physical environmental conditions such as rain and mud. As the amount of noise in the image increases, edge recognition algorithms produce parallel and dashed lines that make text recognition difficult. In this study, a supporting method for edge detection-based text recognition algorithms is proposed. With the proposed work, block-based Shannon Entropy is applied in the low frequency sub-band of the noisy image. In the experimental results, it was observed that the parallel and dashed lines produced by the edge recognition algorithms in the noisy image decreased. The proposed algorithm is not efficient in terms of algorithm complexity for noise-free images, but it can contribute positively to edge recognition in noisy images.
... Seven scales are used for the Haar wavelets, although similar results are obtained using fewer scales. Coefficients can be computed in the horizontal, vertical, and diagonal directions, and we use horizontal coefficients that emphasize horizontal edges (59). ...
Individual paintings by artists including Vincent van Gogh and Edvard Munch have been shown to depict specific atmospheric phenomena, raising the question of whether longer-term environmental change influences stylistic trends in painting. Anthropogenic aerosol emissions increased to unprecedented levels during the 19th century as a consequence of the Industrial Revolution, particularly in Western European cities, leading to an optical environment having less contrast and more intensity. Here, we show that trends from more figurative to impressionistic representations in J.M.W. Turner and Claude Monet's paintings in London and Paris over the 19th century accurately render physical changes in their local optical environment. In particular, we demonstrate that changes in local sulfur dioxide emissions are a highly statistically significant explanatory variable for trends in the contrast and intensity of Turner, Monet, and others' works, including after controlling for time trends and subject matter. Industrialization altered the environmental context in which Turner and Monet painted, and our results indicate that their paintings capture changes in the optical environment associated with increasingly polluted atmospheres during the Industrial Revolution.
... Sunsets seen through an aerosol-laden stratosphere appear redder because of greater scattering in the limb of Earth's atmosphere (11). Across schools of painting, the red-to-green ratios in sunset paintings from 1500-1900 are correlated with independent proxies of stratospheric aerosol content (4,5), though difficulty constraining the aerosol size distribution and solar zenith angle introduces uncertainties to this methodology (12). ...
... Seven scales are used for the Haar wavelets, although similar results are obtained using fewer scales. Coefficients can be computed in the horizontal, vertical, and diagonal direction, and we use horizontal coefficients that emphasize horizontal edges (e.g., 12). ...
Individual paintings by artists including Vincent van Gogh and Edvard Munch have been shown to depict specific atmospheric phenomena, raising the question whether longer-term environmental change influences stylistic trends in painting. Anthropogenic aerosol emissions increased to unprecedented levels during the 19th century as a consequence of the Industrial Revolution, particularly in Western European cities, leading to an optical environment having less contrast and more intensity. Here we show that trends from more figurative to impressionistic representations in J.M.W. Turner and Claude Monet's paintings in London and Paris over the 19th century accurately render physical changes in their local optical environment. In particular, we demonstrate that changes in local sulfur dioxide emissions are a highly statistically-significant explanatory variable for trends in the contrast and intensity of Turner, Monet, and others' works, including after controlling for time trends and subject matter. Industrialization altered the environmental context in which painting occurred, and our results indicate that Impressionism contains elements of polluted realism.
... The HL image contains the vertical features of the edge; while the LH contains the horizontal features of the edge. The LL, which is also called approximate image, contains most of the recognizable information of the original image and therefore it is the only one that is used as the input image to another level of DWT (Mistry, 2013). ...
... The HL image contains the vertical features of the edge; while the LH contains the horizontal features of the edge. The LL, which is also called approximate image, contains most of the recognizable information of the original image and therefore it is the only one that is used as the input image to another level of DWT (Mistry, 2013). ...
nowadays, image encryption is an interesting research area due to the rapid growth of communication technologies and the increasing demands for safeguarding the privacy of the transmitted images as unencrypted images are vulnerable to interception. Partial encryption is used to overcome the challenge of encryption the large size of digital images, due to the large computational overhead and long processing time needed. In this research, one level Discrete Wavelet Transform is applied to a colored image to divide the image to four sub-bands; then a texture segmentation using a Gabor filter and K-means clustering is applied to lowest frequency band. To achieve that, the texture segmentation is used as a new method for scrambling the image based on the features of another image. Finally, the scrambled segments of the image are encrypted using interchanged AES and RC4 Algorithms. Each one of the image pixels is then XORed using one of the two algorithms and the key of the other algorithm. The proposed work is evaluated using several security and performance analysis tests. The results show that the proposed work is resistant agents many kinds of attack and promising in real-time image transmission and encryption.
... The second phase is embedding an encrypted image ′ into reference image . This embedding is performed in frequency domain through Discrete wavelet transform (DWT) [15][16][17]. In this, the reference image is decomposed into four matrices denoted as a (approximation matrix), h , and [5]. ...
Due to advancements in technology, digital images are widely used in various applications like medical field, military communication, remote sensing, etc. These images may contain sensitive and confidential information. Therefore, images are required to be protected from unauthorized access. Many image protection techniques have been proposed in past years. The most common technique to protect the images is encryption. In this technique, a secret key and an encryption algorithm are used to change the plain image into an encrypted image. The encrypted image looks like a noisy image and can easily attracts the attacker’s attention. If image gets captured and stacked, sensitive information can be revealed. In this regard, Visually Meaningful Encrypted Image (VMEI) technique is developed, which initially encrypts the original image and then hides it into a reference image. The final encrypted image looks like a normal image. Hence, VMEI technique provides more security as compared to simple image encryption techniques. Therefore, a systematic survey on existing VMEI techniques is presented in this paper. The VMEI techniques are divided into different categories based on their characteristics. Moreover, this paper elaborates and investigates the improvements and analyses performed on VMEI techniques based on various evaluation parameters. These evaluation parameters are divided into different categories such as security attacks, encryption key attacks, quality analysis, and noise attacks. Finally, this paper discusses the potential applications and the future challenges of VMEI techniques.
... In recent years, a number of new noise reduction techniques have emerged. One of the first works in this area was devoted to cleaning the ECG signal from additive filter noise [1][2][3][4][5][6][7][8][9]. In the literature in In the present work, processing of an electro cardio signal using a wavelet transform is considered. ...
... The English word wavelet (from the French "ondelette") literally translates as "short wave". In various translations of foreign articles, there are also terms such as: "burst", "burst function", "low-wave function" and others [1][2][3][4]. ...
In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.
... It is concluded that Fourier methods are more useful in case noisy images or if we want high computational speed over correlation method. [13,14]-In wavelet based method features are obtained by using feature detection algorithm rather than intensity values. Wavelet transform can be represented as an intermediate between Fourier and spatial representation. ...
In Image registration process two or more images are aligned. Images used in this process are called (i) referenced image which is used as a reference and remains unchanged. (ii) Target or sensed image which is aligned to reference image. Geometric transformation functions are used so that mapping of location of target or sensed image into referenced image can be done by using control points of images. In image processing, registration can be done to match two or more images captured either at different times, from different sensors, or from different viewpoints. So based on these image registration applications can be categorised as multiview analysis, multitemporal analysis, multimodal analysis, Scene to model registration. We present a comprehensive review on various existing image registration techniques available in literature. Large number of applications based on image registration have been found like target recognition, matching stereo images to recover shape for navigation, satellite images are used for surveillance purposes, CT and MRI images are used in medical diagnosis.
... A good data compression technique named PCA developed in [10] that reduces the number of dimensions, however it suffers spectral degradation. In [11], author has proposed a Discrete Wavelet transform technique that achieves better and precise results with fast computation but it suffers with spatial degradation that reduces the overall performance of output image. In [12], author has implemented the Discrete Stationary Wavelet transform that minimizes spectral degradation in comparison to DWT. ...
Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg's uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.
