Noise can be attenuated by pronounced colours, which are far from the too much noticed or crying colour tones, . But it is not just the colours that can attenuate noise; paintings can create an even stronger impression. Therefore, paintings are becom- ing very frequent decorations in offices and corridors of administrative buildings. We can expect that paint- ings will in the near future decorate industrial halls and workshops as well. The art of painting can mean much more than just a cultural need of a certain environment. With colour and form, and aesthetics of space, tools, and machines, we can achieve a soothing of the negative effects of noise. Good choice of colour is not a simple business. It is not possible to directly measure the benefits of colour, which can be still discovered through feelings (intuitively). Therefore decisions on colour should be entrusted to the designers or artists intuition, the intellect playing a minor role. If, when hearing an undesirable sound (noise) in the presence of a colour or a picture, we are seduced into a world of pleasant associations, the desired effect is achieved. With the interaction of audible and visible signals under cer- tain conditions the ear and eye adapt the outer phenom- ena to their own physiological demands.
The Leq noise level for traffic stream is 98.4dBA where as the standards were 55dBA. The noise levels were 75% more than the CPCB values. Here the no of vehicles using the high way was more. There is huge amount of trucks and buses were operated. Since the intersection is an connection to local area large people were using the junction. Here the pedestrian rate is around 325 persons per hour who were crossing the junction. It is high time evaluate a meaningful noise reduction technique so that people will not be able to get affected by noise pollution. Similar trend is continued in recreation malls and factory places where the Leq levels are higher than the standards. If we see the results of silence zones such as ODR and river beds the results are interesting. In ODR which is a local road connecting two places (avadi and alamathi) here we have traffic by buses and trucks but the no of vehicles and the traffic intensity is very less. Thats why the leq levels were found to be equal to the standards. The river beds which is no man‟s land were very much less than the standards only the wind noise is reflected on the noise readings. This two places show that the intensity received by the people will be very less when compared with other sources on noises through automobiles and others.
MS images. The quality of the source images directly affects the quality of the fused images. Infrared and Visible images are fused together to enable the visualization of the concealed weapons by sensing the thermal radiations emitted by the metallic objects. SAR images are generally prone to speckle noise, Gaussian Noise is generally found in PAN images. The role of Gaussian becomes of great importance in case of vegetation mapping as the image details are inherently masked which are required to be extracted for image interpretation and analysis. The various spreads and colours and indexing is used in vegetation mapping image sources, to predict entire set of various estimates for several attributes of vegetation mapping. Similarly in case of PAN images, the quality of these images is significant as they required to be fused with MS and HS data. Also, as an independent data source, PAN images, provide the high resolution information, which helps in assessing the structural formation and planning of various sectors of urban planning and development.
The Adaptive Median Filter performs spatial processing to determine which pixels in an image have been affected by impulse noise. The Adaptive Median Filter classifies pixels as noise by comparing each pixel in the image to its surrounding neighbor pixels. The size of the neighborhood is adjustable, as well as the threshold for the comparison. A pixel that is different from a majority of its neighbors, as well as being not structurally aligned with those pixels to which it is similar, is labeled as impulse noise. These noise pixels are then replaced by the median pixel value of the pixels in the neighborhood that have passed the noise labeling test.
In this paper, noise classification is done using autocorrelation function. The noisy signal is said to be stationary if the autocorrelation function is time invariant, otherwise the noisy signal is said to be non stationary signal. Along with this the other statistical parameters are determined. The characterization of noise signal is done by determining the mean, variance and SNR
There are some limitations to this research, which are in part due to a small sample size and restrictions of the data collection. For the model testing, a larger sample size is preferred as χ² is highly sensitive to sample size. Additionally, as this study tried to test a model, causality has to be assumed which cannot be done using survey studies. Reverse time-order and confounding influences cannot be ruled out for this research. Furthermore, this research would have benefitted from objective noise measurements (noise pressure level in dB) to compare these to the noise annoyance and noise disturbance variables and use these as input for the rest of the model. Additionally, complaints or complaint behavior would have been a more accurate outcome variable than complain intentions or intentions to take action.
Image Processing may be contaminated with distinctive types of noise for distinctive motives. As an instance, noise can occur due to the situations of recording which includes digital noise in cameras, dirt in the front of the lens, due to the fact of the occasions of transmission damaged statistics or due to garage, copying, scanning, and so forth. Impulse noise e.g., salt and pepper noise and additive noise e.g. Gaussian noise are the most generally observed. Impulse noise is characterized by the reality that the pixels in an photograph both continue to be unchanged or get one of the two unique values zero and 1; an important parameter is the noise density which expresses the fraction of the Image pixels that are contaminated.
Noise Mapping is a study made to differentiate the city into zones according to different Noise levels. It records Noise as is actually present in a location and compares it to the ideal noise levels, as stipulated by the standards given. A study was conducted on the roads of Mumbai city using a Sound Level Meter (SLM). This data was tabulated to demarcate the city into different noise zones. The Leq, Noise Climate, and Noise pollution levels were calculated . This was further represented in the form of cartographic maps for easy understanding. It was found that the noise levels in overall city were very high and above the permissible limits. The average values throughout were 70-80dB .
For proper modelling of signal and noise in MR data requires proper interpretation and analysis of data, the different approaches with this degradation due to random fluctuations in the MR data, probabilistic modeling is power solution, which needscorrectnessin thecomputation of noise is challenging task and various stastical approaches can be utilized. After modelling the noise it can be integrated to denoising pipeline, in this research work, the recognition of noise only pixels and the evaluation of standard deviation of noise using median, mean or other optimal sample quantiles are combined in to single frame work for noise assement and uses fixed point iterative procedure to obtain standard deviation of noise. We tested the effectiveness of the algorithm to the MR clinical and synthetic data base.
The DAR strategy mainly contains two stages, i.e. the detecting stage and the replacing stage. In the detecting stage, algorithms are designed to find noise corrupted samples; in the replacing stage corrupted samples are updated by approximate values estimated from reliable samples. Specifically, there are mainly two classes of impulse detecting algorithms, i.e. the statistical model based (SMB) algorithms and the threshold based (TB) algorithms. In SMB algorithms both the noise process and clean speeches are assumed to obey certain probability distributions , . Then Bayes’ rule is applied to obtain the posterior distribution of noise locations, and the final detecting results are given by the well-known MAP estimator. Quite differently, TB algorithms consider the discontinuous nature of noise corrupted waveforms , . By comparing the sample amplitude (or its differential version) with a well-tuned threshold, TB algorithms algorithms regard the exceeded samples as being corrupted by impulse noise. The replacing algorithms also fall into two categories, i.e. the autoregressive (AR) modeling algorithm and sparse modeling algorithms. In the AR modeling approach, estimates of original samples are obtained by minimizing sum of squares of the residual errors that involve estimates of the AR parameters ,. On the other hand, sparse modeling algorithms generally utilize the frequency-domain sparsity especially in the voiced part 
Noise is unwanted sound that is harmful, annoying, causes disturbance and may adversely impact the work efficiency and hearing. Increased activities and needs in modern urban life are causing severe noise pollution. Noise pollution due to major source of pollution in urban areas. Fast growing vehicle population in urban regions in the recent years, has resulted in tremendous increase in traffic on roads causing alarming noise pollution, besides air pollution. Traffic noise is ctors like traffic volume, vehicle mix, pavement type and vehicle condition (Marathe, 2012). Noise level increases with traffic volume in an exponential manner and depends on several parameters such as source, medium, vehicle ce from source etc. (Vilas and Nagarale, 2013, Suhas and Adavi, 2015). Hence, the overall noise is dependent on the characteristics of the vehicle and the relative proportions of the vehicle types included in the flow. the “threshold of hearing” and the “threshold of pain”. In terms of pressure, this 20 kPa (Garg, 2014). Noise level is measured in terms of decibels (dB). The Noise levels are measured using a sound level meter and calculated values such as L10, L50, L90, are used to estimate equivalent value of sound level (Leq) while Traffic Noise Index (TNI), Noise Climate (NC), Noise
3.1.1. Helicopter noise generation differs from fixed wing propeller driven aircraft because the main rotor and tail rotor operate close to the horizontal plane and vertical plane, respectively, with axes of rotation normal to the flight direction. Whilst for propeller driven aircraft the axis of the propeller is aligned to the direction of travel, and the noise from each propeller generally has symmetry about this axis. Such axial symmetry does not exist for helicopter rotor blade noise sources. For this reason very few of the helicopter noise sources are similar to that of its fixed wing counterparts . 3.1.2. Helicopter noise is generated from a number of main sources: engine noise, rotor noise and transmission noise. Apart from piston engine powered craft, the main noise sources are from the rotors . Spectral analysis of helicopter noise reveals a series of tones generated by the main and tail rotors. The main rotor generates a series of tones whose fundamental is in the range 10 to 40Hz. The tail rotor generates a higher frequency tone series whose fundamental is usually in the range 100 to 200Hz . Although the tonal noise dominates, broadband noise from both the tail and the main rotors is present at a lower level. There also exist interactive effects between tail and main rotors and the fuselage, the former interaction leads to combination tonal frequencies known as ‘Burble’. Impulsive sounds also result from the blade tips intercepting the vortex from a preceding blade (Blade Vortex Interaction - BVI) or the vortices from the main rotor being intercepted by the tail rotor (Tail Rotor Interaction - TRI). In addition there exists high speed impulsive (HSI) noise
noisy. It can be seen from the Table 4 that the proportion of individuals exposed to environmental noise levels exceeding the standard values in most of the selected study locations during day time. From this study, it was observed that at residential area, noise level varied from 67.7 dBA to 77.1 dBA which should not exceed 55dB (A) as per Indian standard. As previously stated that the guideline value above have recommended by ARAI in India, noise emissions should not exceed 65 dBA during daytime in commercial area but in this survey this value reaching maximum up to 87.4 dBA and Leq is 82.3 dBA. There is need to control noise pollution in Roorkee.
Casing vibrations were investigated in three di- rections: Positive ydirection in the pump axial direction (in the direction of the intake pipe axis), positive zdirection in the pump radial direction, and positive xdirection in a direction tangentional to the impeller exit diameter. A B&K system type 4321 was used for vibration measurements, and an RFT 2218 measuring system for the noise measure- ments . For the frequency spectrum (and the power spectrum) a data acquisition system (DAQ) was used. The system consists of a PC with a Multifunctional PCI-2048W board card Intelligent Instrumentation, and software Visual Designer. The sampling frequency used in the ex- periment was 6 kHz per channel.
Where g(x, y) is the original image & f(x, y) is observed image, both expressed in vector form, K is the blurring/convolution operator represented in matrix form, which is supposed to be known and ɳ is noise vector having distribution independent and identically with mean noise and standard deviation σ. Blur and noise are the two main degradations by which the quality of image gets ruined. Noise such as Gaussian noise, multiplicative noise, Poisson noise and impulse noise affects the image and leads to loss of information. On the other hand various blur models such as atmospheric blur, uniform blur, motion blur and Gaussian blur are also alter the image information. Presence of noise and blur results in original information of image gets disturbed. In field of medical imaging the diagnosis process is merely rely on the information present in image, presence of noise and blur has severe effect and should be eliminated. This gives rise to various denoising and deblurring techniques. In the preceding sections we will discuss about various noise and blur models and their effects on images.
After conducting literature survey of various image restoration techniques proposed by different researchers, we can conclude that deblurring blur from images is a problem that is difficult to resolve, however to some extent the above techniques such as Lucy and Weiner Filter give better results. We also tried to apply neural networks on these techniques. In this paper, noise model, blurring and deblurring techniques are elaborated and their merits and demerits are explored.
bring as much as 3 dB reduction in peak noise during take-off with less than 0.5 % thrust loss during cruise. For high frequencies and large angles to the jet, the use of chevrons may also direct to about to 2 dB noise increase. This naturally leads to the chevron design optimization problem in which eddy resolving numerical simulations and acoustic modeling techniques for jet noise prediction play an important potential role. Hao Xia (2013) carried out numerical study of chevron jet noise using parallel flow solver. Author performed cross large-eddy type simulations for chevron nozzle jet flows at Mach 0.9 and Re ~ 10 5 . Many researchers carried out studies on chevron nozzles for various applications. Fan Shi Kong, Heuy Dong Kim, Yingzi Jin and Toshiaki Setoguchi (Fan Shi Kong, 2013) reported a new class of nozzle with chevrons was installed inside the supersonic ejector- diffuser system. Literature review further reveal that the nozzle with chevrons was widely used in the aerospace science and aircraft engine, because it has many advantages such as jet noise drop infrared signature control and enhancement of conventional converging-diverging nozzle or convergent nozzle (Khalid et al., 2010). Gregory A. Blaisdell et al. (2011) also view that the conventional nozzle features were improved as a result of installing the chevrons.
mapping enables to locate the effects of noise pol- lution in X, Y and Z direction on any residential building or setup. Most of the researchers used GIS as a tool for development of 2D noise map in different countries like Taiwan, Netherlands, Russia, Poland, Turkey, Kenya, Spain, Nigeria, Portugal and Egypt. In some countries, research- ers used other tools for the development of noise maps; for instance, Nasim Akhter et al. (in India), and Zannin et al. (In Brazil) used soundPlan for the development of 2D and 3D mapping. In Chi- na, [Wu, et al., 2018] used Swallow sound for the development of a 2D noise map for the selected locations. In Latin America [Fiedler and Zannin, 2015], used Predictor 8.11 for the development of 2D and 3D noise mapping for the selected loca- tion of the Curitiba city. CAD 3D software has also been used in two countries i.e., in Spain and Brazil, in Madrid and Brasilia, respectively, for the 2d noise mapping only. Most of the research- ers have developed 2D noise maps only for the selected locations of different countries such as [Kartikey Tiwari et al., 2017] for India, [Tsai et al., 2009] for Taiwan, [Paulo and David, 2011] for Brazil, [Wu, 2015] for China, [Vasilyev, 2017] for Russia, [Awadhi and Kandary, 2017] for Kuwait, [Dursun et. al., 2006] for Turkey, Brainard et al., 2004 for United Kingdom, [Wawa and Mulaku, 2009] for Kenya, [Arana et al. 2009] for Spain, [Coelho and Alarcao, 2005] for Portugal, [Eldien, 2009] for Egypt, [Nicolas et al., 2016] for Chile, [Olayinka, 2012] for Nigeria, and [Farcaş and Sivertunb, 2015] for Sweden. Few researchers have developed 3D noise maps for a selected lo- cation of some countries, such as [Nasim Akhtar et al., 2016] for India, [Stoter et al., 2008] for the Netherlands, [Kossakowski, 1990] for Poland, [Fiedler and Zannin, 2015] for Latin America. As per the above literature review of 2D and 3D noise mapping, it has been established that the 2D noise maps have been developed by most of the researchers for their respective developing coun- tries to find out the distribution of noise along a central line of a road or along the periphery of an industry. However, the literature survey also shows that the 3D noise gives a clear picture of the noise distribution in all three directions X, Y, and Z. In one of the studies of India, 3D noise maps have been developed by [Akhtar, et al., 2016] for the selected location of Delhi which gives clear picture of noise distribution in all three directions and also provides a number of the people affected in a particular residential building. Thus, from the
Abstract— Noise is always presents in digital images during image capturing, coding, transmission, and processing steps. The performance of imaging sensors is affected by a variety of factors, such as environmental conditions during image capturing, and by the quality of the sensing elements them- selves. For instance, in capturing images with a CCD camera, light levels and sensor temperature are major factors affecting the amount of noise in the resulting image. Images are corrupted during transmission principally due to interference in the channel used for transmission. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image noise-reduction techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this paper we present results for different filtering techniques and we compare the results for these techniques. Noise removal is an important task in image processing. In general the results of the noise removal have a strong influence on the quality of the image processing techniques. The nature of the noise removal problem depends on the type of the noise corrupting the image.
Wiener2 is a 2-D adaptive noise removal filter. The wiwner2 function applies a wiener filter which is a type of linear filter to an image adaptively, tailoring itself to local image variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing. This approach often produces better result than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high frequency parts of an image. In addition, there are no design tasks; the wiener2 function handles all preliminary computations, and implements the filter for preliminary computations, and implements the filter for an input image. Best suitable to remove Gaussian noise.