Communication refers to a way that links people, as it is a two-way procedure that enables partakers to exchange ideas, information, feelings, and news with the aim to share or generate a meaning. Communication noise is regarded as an influence on proficient communication that manipulates or persuades the elucidation of a conversation. Forms of communication noise include; environmental noise, psychological noise, physical noise, and semantic noise. An image is a visual representation of an object that aids in bringing out the meaning or idea that is intended to be put across. It is apparent that it is almost inevitable to negate noise in various forms of communication. Consequently, it is important to establish the best way of inferring noise in the communication process in order to avoid distortion of meaning of the information that is being put across.
In the contemporary society, it is notable that digital image systems are often and extensively used. Hence, it is important to understand the dynamics that are associated with digital image scheme, in order to put in place mechanisms that will prevent distortion of information and ideas being passed on. Therefore, understanding the principles, hypothesis, and standards that are in line with visual communication is significant. This is due to the fact that understanding the underlying aspects will help and guide us in establishing an efficient and proficient digital imaging system that captures all the necessary factors and systems (Alonso, Moreno, and Alda, 2004). Also, it is important to appreciate that there are various types of noises that are associated with visual communication. Recognizing the aspect of communication noise in imaging process will enable us to determine the best way of perceiving the noise accompanying visual communication with the aim to avoid distorting the information that is being projected. Therefore, indulgent and understanding are key factors in promoting proficient visual communication.
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Noise is often there in the digital images during various stages of image dispensation such as; acquisition, coding, transmission, and processing. It is obvious that eliminating noise from visual communication is difficult without preceding knowledge to noise models. Therefore, reviewing noise models is significant in the process of denoising schemes (Boyat, and Joshi, 2015). Consequently, it is important to analyze various noise models, in order to put in place measures that will aid in minimizing unpleasant noise in image processing scheme. All stakeholders and policy makers in the field of image denoising need to undertake a harmonized evaluation and review in regard to noise theories and techniques. This will help in coming up with the best noise denoising schemes that are geared towards minimizing distortion of information and meaning. Furthermore, it is important for researchers to undertake the initiative to establish the practicability of denoising schemes and the possible adjustment to make them more effective.
It is important to often enhance the skills of practical and hypothetical ideas in order to determine forms of noise that are denoted in digital images (Alberti, 2006). Noise is considered as a random indication that obliterates some aspects of image information. The various types of noise that distort image information are Poisson noise, Gaussian noise, Speckle noise, Salt and Pepper noise. The noises that are associated with an image may be originating from a noise source that is within the environs of an image capturing device, faulty in reminiscence setting and deficiency in the image capturing gadget. Therefore, it is important to evaluate and establish the origin of the noise that is coming along with the image. The undertaking will aid in putting in place the best denoising technique that will counter the noise, as its originality is put into account. Therefore, it is obvious that determining originality of the noise arising is significant in mitigating the aspect of information distortion during imaging process.
Noise fabricates unattractive effects such as impractical edges, artifacts, blurred objects, unnoticed lines, and corners. Minimizing the unwanted effects in images, demands that we get into the noise models. Therefore, it is significant to venture into various models in order to get the insight of each noise model. Gaussian noise model is also referred to as electronic noise due to the fact that it takes place in detectors or amplifiers. Gaussian noise comes into play due to natural sources which include distinct nature of emission of warm objects and thermal vibration of atoms. The underlying noise universally distorts the gray value of digital images. Impulsive noise model is also referred to as data drop due to the fact that it statistically plunges the original data values. Periodic noise arises from electronic hindrances, particularly in power signal during the stage of image acquisition. Periodic noise can be eliminated via using a slight band rebuff filter or indentation filter (Boyat, and Joshi, 2015).
Photon noise comes to existence due to the fact of statistical scenery of electromagnetic waves such as x-rays, gamma rays, and visible lights. Structured noise is stationary or non stationary, periodic, and aperiodic in nature. In instances where this noise is stationary it is clear that it has a rigid amplitude, phase, and frequency. This model is inclined towards bursting rank dimension space in corporeal system. Gamma noise is inferred in laser pedestal images, due to the fact that it complies with the principle of gamma distribution. Speckle noise is considered as multiplicative noise that is perceived in rational imaging scheme such as acoustics, radar and laser. Quantization noise manifestation is intrinsic in amplitude quantization procedure. It is generally in existence due to the fact that there is an undertaking that presents a platform for converting analog data to digital data (Boyat, and Joshi, 2015). It is notable that there may be an overlap of various forms of noises in a particular imaging system. Therefore, it is important to undertake a critical analysis in each situation in order to come up with the best solution.
Color visual segmentation is a theme that has attracted various debates in respect to color image analysis and pattern recognition. Several artistic means have been put in place with the aim of addressing color segmentation in imaging systems. The challenge that arises in this field is over-segmentation and misclassification. The driving force behind this is dilapidation of visual quality at the stages of acquisition, broadcast and color space adaptation. The underlying challenges in color image segmentation bring into context the aspect of image enhancement techniques. The techniques are geared towards making the visual quality more proficient with the aim of establishing the best image segmentation procedure. Color image enhancement techniques aid in eliminating outliers and unpleasant noise influencers that may distort the interpretation of an image (Bora, 2017). Investigations indicate that the most effective undertaking is to introduce a model or system that removes the noises to the possible minimum levels. Furthermore, it is notable that noise can be completely eliminated when its cause and formation is deliberated on and evaluated.
Understanding features of image operations is significant in enhancing production of visual communication that is more proficient. Also, it is important to be acquainted with the various classifications of image operations. Recognizing the features and classification helps us to understand the outcome that we are anticipating in a particular scenario Young, Gerbrands, and Vliet, 2007). This is important as it helps us to put in place mechanisms and measures that will enhance effective step ups in the process of visual representation. Furthermore, it is important to be acquainted with various challenges and problems that are associated with each particular image operation. This will aid in establish the best mechanism of dealing with each situation regardless of the type of visual operation that is connected with it. Consequently, it is imperative to be precise and concise in denoting classification and features of image operations in order to avoid misrepresentation.
There are several types of operations that can be put in place with the aim to transform an input representation into an output illustration in the digital image system. Point operation ensures that the output depiction at a particular coordinate is dependent only on the input illustration at the same coordinate. Local operation ensures the output illustration at a certain coordinate is reliant on the input depiction in the neighborhood of that identical coordinate. Global operation ensures that the output indication at a particular coordinate is reliant on all the depictions in the input reflection (Young, Gerbrands, and Vliet, 2007). Therefore, it is apparent that each image operation has its underlying principles governing its functionality. Consequently, it is important to understand the principles that are aligned towards each image operation in order to avoid confusion and misrepresentation in visual communication.
In conclusion, it is notable that there are types of noise models that are connected to image processing or visual communication. Therefore, it is important to understand the aspects and principles of visual communication in order to establish the noise that is involved in various stages of image processing. Also, the aspect of evaluating and understanding various noise models is significant as it enables us to establish the model that is depicted in a particular instance. In addition, it is important to establish the originality of noise in an image processing procedure, as it will aid in putting place the best denoising mechanism. Furthermore, the study denotes that evaluating features and classification of image operation is significant as it aids in bringing out the actual representation. Therefore, we have to understand all principles, standards, regulations, and rules that are associated with image in order to take the art to the next level.
References
Alberti, W. P. (2006). The Anatomy and Physiology of the Ear and Hearing: retrieved from: http://www.who.int/occupational_health/publications/noise2.pdf
Alonso, M. J., Moreno, G. R., & Alda, J. (2004). Noise in Imaging Systems: Fixed Patterns Noise, Electronic and Interference Noise: retrieved from: https://www.researchgate.net/publication/228794563_Noise_in_imaging_systems_fixed_ pattern_noise_electronic_and_interference_noise
Bora, J. D., (2017). Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study: retrieved from: https://arxiv.org/ftp/arxiv/papers/1708/1708.05081.pdf
Boyat, K. A., & Joshi, K. B. (2015). A Review Paper: Noise Models in Digital Image Processing: retrieved from: https://www.researchgate.net/publication/276296472_A_Review_Paper_Noise_Models_i n_Digital_Image_Processing
Young, T. I., Gerbrands, J. J., & Vliet, J. L. (2007). Fundamentals of Image Processing: retrieved from: http://www.imageprocessingplace.com/downloads_V3/root_downloads/tutorials/Image% 20Processing%20Fundamentals--An%20Overview.pdf
