Digital Image Processing Steps Image . Weve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data. Sharpening is very specific to output, so it should be the last thing you do in a filtering workflow. Click here to review the details. By accepting, you agree to the updated privacy policy. Smoothing Spatial Filters: also called lowpass filters. Filter Families: Smoothing. This video also talks about box filters, weighted average filters, Gaussian filters, median filters, min and max filters which come under linear and non-linear Smoothing filters. It uses a weight value of 2 in the center. They include: 1.1 Averaging linear filters 1.2 Order-statistics nonlinear filters. It returns h as a correlation kernel, which is the appropriate form to use with imfilter(). 6.5.3 Color Slicing 431 6.5.4 Tone and Color Corrections 433 6.5.5 Histogram Processing 438 Smoothing and Sharpening 439 6.6. . DETAIL RUANG POMPA UP DATE 19-03-22-composit PL.pdf, Managing_Waste_Plastic_by_utilization_in.pptx, synergetic framework for eyeball mouse and gesture recognition (1).pptx, Pole-Zero Placement Method of Filter Design.pptx, No public clipboards found for this slide. By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. By. Image filtering can be grouped in two depending on the effects: When filtering an image, each pixel is affected by its neighbors, and the net effect of filtering is moving information around the image. This is usually obtained by removing noise while sharpening details and improving edges contrast. Order Statistics (Non-linear) filter Kindly like, share and subscribe if you like the video!Check out our previous videos!Introduction to digital image processing - https://youtu.be/J-KxVvDRl18 Key stages in digital image processing - https://www.youtube.com/watch?v=8ekTeZD_lNYSampling and Quantization in digital image processing - https://youtu.be/KWc9SOOLfLwRelationship between pixels Neighbourhood and Adjacency of Pixels-https://youtu.be/JDsuds2oIF8Distance Measures Between Pixels with examples- https://youtu.be/NOIlN9BexpkArithmetic Operations and Logical Operations between Images in digital image processing-https://youtu.be/kTxKca5i5tQPoint operations in digital image processing with examples - https://youtu.be/FMDmXz6ynvkContrast Stretching and intensity level Slicing in digital image processing with examples -https://youtu.be/YJIgFMoC_ygLogarithmic Transformation and power-law Transformation in digital image processing with examples -https://youtu.be/jfoXoFoDb48Image Enhancement in digital image processing with Histogram Equalization -https://youtu.be/Yd6QISby8kkHistogram matching in digital image processing - https://youtu.be/r565euxWZBsFundamentals of Spatial Filtering in digital image processing - https://youtu.be/eDyJsFHYFFI Also note that the medfilt2() is 2-D filter, so it only works for grayscale image. Clarification: Smoothing filter replaces the value of every pixel in an image by the average value of the gray levels. The International Journal of Engineering and Science (The IJES), Literature survey on impulse noise reduction, Novel adaptive filter (naf) for impulse noise suppression from digital images, A STUDY OF SPECKLE NOISE REDUCTION FILTERS. Spatial filtering is the traditional method of image filtering. Triunelveli. I think the main reason is the "scaling". Looks like youve clipped this slide to already. AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017, Pew Research Center's Internet & American Life Project, Harry Surden - Artificial Intelligence and Law Overview, FINAL_PRESENTATION_2022_labour Productivity_Biprendra.pptx, CATEGORIZATION OF FACTORS AFFECTING CLASSIFICATION ALGORITHMS SELECTION. This video also talks about box filters, weighted average filters, Gaussi. Using Gaussian filter/kernel to smooth/blur an image is a very important tool in Computer Vision. Like other convolutions it is based around a kernel, which represents the shape and size of the neighborhood to be sampled when calculating the mean. Background - what is filtering: Most people understand what filtering is intuitively. Let's test it. Blur the image. Gaussian smoothing has the attribute that the frequency components of the image are modified in a smooth manner. 0 . Blurring is used in preprocessing tasks such as removal of small details from an image prior to object (large details) extraction. Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. In the spatial domain, neighborhood averaging can generally be used to achieve the purpose of smoothing. In this video, we talk about Smoothing Spatial Filters in digital image processing. contactus@bogotobogo.com, Copyright 2020, bogotobogo It's fspecial(): h = fspecial(type) creates a two-dimensional filter h of the specified type. Did you try www.HelpWriting.net ?. Analysis of Non Linear Filters with Various Density of Impulse Noise for Diff IJET - International Journal of Engineering and Techniques, Random Valued Impulse Noise Elimination using Neural Filter. We've encountered a problem, please try again. The SlideShare family just got bigger. digital image pro. Frequency domain filters are use to remove high and low frequencies and smoothing.. APIdays Paris 2019 - Innovation @ scale, APIs as Digital Factories' New Machi Mammalian Brain Chemistry Explains Everything. Digital Image Processing Steps Image Processing. The selective sharpening filter can be implemented quickly by using implementations of the existing selective smoothing filter and a derived matching non-selective . Digital Image Processing Spatial Filtering. By accepting, you agree to the updated privacy policy. Updated on Jul 15. All smoothing filters build a weighted average of the surrounding pixels, and some of them also use the center pixel itself. Tap here to review the details. Find the treasures in MATLAB Central . V.Praseetha For example, the Laplacian linear filter. list=PLXOYj6DUOGrpVb7_cCB1pZuGH4BFlp61B#DigitalImageProcessing https://www.youtube.com/playlist? Smoothing spatial filters are digital image processing filters that are used to blur an image. Image smoothing is a key technology of image enhancement, which can remove noise in images. spatial domain fi. COM2304: Intensity Transformation and Spatial Filtering III Spatial Filters Research Scholar in Manonmaniam Sundaranar University, International Journal of Computational Engineering Research(IJCER), TYBSC (CS) SEM 6- DIGITAL IMAGE PROCESSING, Iaetsd literature review on efficient detection and filtering of high. Digitization of the spatial coordinates (x,y) is called image sampling. So, let's look at how image enhancement occurs in the spatial and frequency domains. Dynamsoft Barcode Reader (DBR) is an enterprise-grade barcode scanner SDK (Software Development Kits). Much better. Image enhancement algorithms offer a wide variety of approaches for modifying original captured. Low pass filtering (aka smoothing), is employed to remove high spatial frequency noise from a digital image. Both. Free access to premium services like Tuneln, Mubi and more. It really depends on the level of which you are trying to deblur the image. Linear smoothing filters: c. Sharpening filters: d. Geometric mean filter: View Answer Report Discuss Too Difficult! Two examples with = 1 is as follows: "Image by Author" "Image by Author" Dr. Sapna Katiyar 11.7K subscribers This lecture describes about the Image Sharpening Spatial Filters. Spatial domain and frequency domain filters are commonly classified into four types of filters low-pass, high-pass, band-reject and band-pass filters. Looks like youve clipped this slide to already. You can read the details below. Gaussian Filter (Gaussian Low Pass Filter) is a popular smoothing filter which is based on Gaussian Distribution where the formula of Gaussian Distribution is as follows with = standard deviation: We can create any size of Gaussian Filter following this formula. SPATIAL DOMAIN list=PLXOYj6DUOGrrAlYxrAu5U2tteJTrSe5Gt#DigitalCommunication https://www.youtube.com/playlist?list=PLXOYj6DUOGrr- O76Jv2JVc7PsjM80RkeS what is image sharpening in image processing. The spatial frequency is related to the concept of texture. Many companies have already benefited from the powerful barcode recognition of DBR and by using it they can. Assistant Professor, It appears that you have an ad-blocker running. Assistant Professor / CSE at Vivekanandha Institute of Engineering and Technology for Women, Tiruchengode, Vivekanandha Institute of Engineering and Technology for Women, Tiruchengode. How about trying the Matlab's built-in median filter? It just made them blurred. Use of second derivative for Image Enhancement The Laplacian. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Sponsor Open Source development activities and free contents for everyone. Campfire Dog (Image by Author) Now the kernels we shall apply to the image are the Gaussian Blur Kernel and the Sharpen Kernel. Digital images are contains various types of noises which are reduces the quality of images. SMOOTHING FILTERS IN 1 Types of Smoothing Filter There are 2 way of smoothing spatial filters Linear Filters - operations performed on image pixel Order-Statistics (non-linear) Filters - based on ranking the pixels 6 7. First, to make the input a little bit dirty, we spray some pepper and salt on the image, and then apply the mean filter: It has some effect on the salt and pepper noise but not much. A method of deriving from an existing selective image smoothing filter either a corresponding filter for selective image sharpening, or a corresponding filter for both selective image smoothing and selective image sharpening. Blurring is pre-processing steps for removal of small details and Noise Reduction is accomplished by blurring. list=PLXOYj6DUOGrqjdqkWSZi4we3Q3oWCvmsW#DigitalElectronics https://www.youtube.com/playlist? We've encountered a problem, please try again. Digital Image Processing Spatial Filtering. So that raises the question, is there a way to automatically learn these types of filters? Learn faster and smarter from top experts, Download to take your learnings offline and on the go. The spatial filters represent another method of digital processing used for the enhancement of an image. Weve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data. Smoothing in Digital Image Processing Pallavi Agarwal Sharpening spatial filters Research Scholar in Manonmaniam Sundaranar University 2.spatial filtering mukesh bhardwaj Spatial enhancement abinarkt Adaptive unsharp masking Ravi Teja Unit3 dip Imran Khan Image enhancement Dr INBAMALAR T M Module 2 UllasSS1 In this lecture we will understand Smoothing spatial filters in digital image processing.Follow EC Academy onFacebook: https://www.facebook.com/ahecacademy/ . This operation is continued for all the pixels in the image. Low pass filters (Smoothing): In order to remove high spatial frequency noise from a digital image, low pass filtering (also known as smoothing) is used. 4. These filters work by averaging the pixels in an image to create a smooth, blurred effect. That's exactly the following script does: Ph.D. / Golden Gate Ave, San Francisco / Seoul National Univ / Carnegie Mellon / UC Berkeley / DevOps / Deep Learning / Visualization. Now customize the name of a clipboard to store your clips. A typical smoothing kernel is shown in Figure 4.23. Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. Digital Image Processing Spatial Nolinear Filter Smoothing. Smoothing; Gaussian; Gradient; Laplacian; Image Smoothing. The techniques used in this study are applicable for real-time processing and can be . I am trying to "translate" what's mentioned in Gonzalez and Woods (2nd Edition) about the Laplacian filter. Answer: D. Clarification: The output as an average of pixels in the neighborhood of filter mask is simply the output of the smoothing linear spatial filter also known as averaging filter and lowpass filter. list=PLXOYj6DUOGroZA7mStdqXWQl3ZaKhyHbO#FlipFlops https://www.youtube.com/playlist?list=PLXOYj6DUOGroXqMKO44k-H54- xVBQjrEX#Opamp https://www.youtube.com/playlist?list=PLXOYj6DUOGrrzy-Nq55l_QZ40b4GP1Urq#ContolSystems https://www.youtube.com/playlist?list=PLXOYj6DUOGrplEjDN2cd_7ZjSOCchZuC4#SignalsAndSyatems https://www.youtube.com/playlist? APIdays Paris 2019 - Innovation @ scale, APIs as Digital Factories' New Machi Mammalian Brain Chemistry Explains Everything. Digital Image Processing Spatial Nolinear Filter Smoothing. A lot of this is derived from the book Digital Image Processing by Rafael C. Gonzalez & Richard E. Woods and can be used as quick refresher. Activate your 30 day free trialto continue reading. AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017, Pew Research Center's Internet & American Life Project, Harry Surden - Artificial Intelligence and Law Overview, Weight and Volume_Battery comparison_SSThakur.pdf, Chapter 5 Getting Started with Python.pptx, FINAL_PRESENTATION_2022_labour Productivity_Biprendra.pptx, 5. ], [2., 4., 2. In order to extract a sharpened view of the edges, we smooth this image using a mean filter (kernel size 33) and then subtract the smoothed result from the original image. Submitted to, 2. Submitted by, Gradient Filter, Laplace Filter, High-boost Filter and Unsharp Masking have also been. By whitelisting SlideShare on your ad-blocker, you are supporting our community of content creators. In this video, we talk about Smoothing Spatial Filters in digital image processing. Digital Image Processing Second Order Derivative Enhancement. D. All of the mentioned. Averaging filter. The process of sharpening is usually used to enhance edges in an image. It refers to the frequency of variation of the different tones that . . Low-pass filters usually use a moving window operator that affects one pixel of the image at a time, modifying its . Mask shapes other than the Gaussian can do odd things to the frequency spectrum, but as far as the appearance of the image is concerned we usually don't notice much. The operation works like this: keep this kernel above a pixel, add all the 25 pixels below this kernel, take the average, and replace the central pixel with the new average value. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. MS University, computer-vision matlab morphology image-processing image-segmentation spatial-filters geometric-transformation radiometric-modication. Sharpening filters are very sensitive to noise. You should note that although these filters can effectively reduce noise, they must be used with care so as to not alter important . Background features can be recovered while still preserving the sharpening effect of the Laplacian operation by simply Instant access to millions of ebooks, audiobooks, magazines, podcasts and more. Digital Image Processing Test M.Madhu Bala As mentioned earlier, the low pass filter can be used denoising. Matlab provides a method to create a predefined 2-D filter. My solutions (and my partner) for the practical work of the Image Processing (Traitement d'image TI) module in my 3rd year of state engineering studies at ENSTTIC. Cancel. Generator step-up and system intertie power transformers Securing high-qualit synergetic framework for eyeball mouse and gesture recognition (1).pptx, Managing_Waste_Plastic_by_utilization_in.pptx, DUAL BAND GNSS ANTENNA PHASE CENTER CHARACTERIZATION FOR AUTOMOTIVE APPLICATIONS, Pole-Zero Placement Method of Filter Design.pptx, No public clipboards found for this slide. BogoToBogo ], Mean filtering is easy to implement. In this article I have notes, code examples and image output for each one of them. Answer: (a . There are many filters that we can use but one that can sharpen our image is represented in a matrix below. Design: Web Master, Digital Image Processing 2 - RGB image & indexed image, Digital Image Processing 3 - Grayscale image I, Digital Image Processing 4 - Grayscale image II (image data type and bit-plane), Digital Image Processing 5 - Histogram equalization, Digital Image Processing 6 - Image Filter (Low pass filters), Video Processing 1 - Object detection (tagging cars) by thresholding color, Video Processing 2 - Face Detection and CAMShift Tracking, The core : Image - load, convert, and save, Signal Processing with NumPy I - FFT and DFT for sine, square waves, unitpulse, and random signal, Signal Processing with NumPy II - Image Fourier Transform : FFT & DFT, Inverse Fourier Transform of an Image with low pass filter: cv2.idft(), Video Capture and Switching colorspaces - RGB / HSV, Adaptive Thresholding - Otsu's clustering-based image thresholding, Edge Detection - Sobel and Laplacian Kernels, Watershed Algorithm : Marker-based Segmentation I, Watershed Algorithm : Marker-based Segmentation II, Image noise reduction : Non-local Means denoising algorithm, Image object detection : Face detection using Haar Cascade Classifiers, Image segmentation - Foreground extraction Grabcut algorithm based on graph cuts, Image Reconstruction - Inpainting (Interpolation) - Fast Marching Methods, Machine Learning : Clustering - K-Means clustering I, Machine Learning : Clustering - K-Means clustering II, Machine Learning : Classification - k-nearest neighbors (k-NN) algorithm, Approximates the linear motion of a camera, Prewitt horizontal edge-emphasizing filter. Bridging the Gap Between Data Science & Engineer: Building High-Performance T How to Master Difficult Conversations at Work Leaders Guide, Be A Great Product Leader (Amplify, Oct 2019), Trillion Dollar Coach Book (Bill Campbell). Sharpening Spatial Filters: also called highpass filters. It is used as a method of smoothing images, reducing the amount of intensity variation between one pixel and the next resulting in reducing noise in images. Clipping is a handy way to collect important slides you want to go back to later. Two further types Smoothing Linear Filters Order-Statistic Filters (Non-Linear) Smoothing Linear Filters Averaging Filter Image processing operations implemented with filtering include . Spatial Domain: Input -> Image Processing -> Output Frequency Domain: Frequency + Distribution -> Image Processing -> Inverse Transformation -> Output Without getting too technical in these areas, we'll shift our focus onto the image enhancement side of this. Smoothing Spatial Filters Try this code and check the result: import numpy as np import cv2 as cv from matplotlib import pyplot as plt A machine understands and manipulates the images based on those numbers only. Smoothing Filters Image smoothingis a digital image processing technique that reduces and suppresses image noises. Spatial Filters Spatial filters can be classified by effect into: 1. This sharpening filter is using a coefficient to smooth the output image while enhancing edges. Multiple choice questions on Digital Image Processing (DIP) topic Intensity Transformations and Spatial Filtering. Filters 156 Sharpening Spatial Filters 157 3.6.1 Foundation 158 3.6.2 Using the Second Derivative for Image SharpeningThe Laplacian 160 . Minimum filtering causes the darker regions of an image to swell in size and dominate the darker regions, Max filtering causes the lighter regions of an image to swell in size and dominate the lighter regions. Activate your 30 day free trialto continue reading. shape is a string with one of these values: Now we want to apply the kernel defined in the previous section using filter2(): We can see the filtered image (right) has been blurred a little bit compared to the original input (left). You should always apply noise reduction first, if necessary. Frequency Domain Filters are used for smoothing and sharpening of image by removal of high or low frequency components. Smoothing Filters. This is done because, random noise typically consists of sharp transitions in gray levels. Blurring means supressing most of high frequency components. Digital Image Processing Second Order Derivative Enhancement. Digital Image Processing Smoothing Spatial Filters. Performance analysis of image filtering algorithms for mri images, International Journal of Computational Engineering Research(IJCER). what is image sharpening in image processing. It appears that you have an ad-blocker running. The development of image processing software helps the image editing process effectively. Tap here to review the details. Clipping is a handy way to collect important slides you want to go back to later. Convolution and correlation, predefined and custom filters, nonlinear filtering, edge-preserving filters. Note that in all the masks shown, the sum is equal to . R.Mathu Sini For this particular filter we don't have an implemented OpenCV function. Enjoy access to millions of ebooks, audiobooks, magazines, and more from Scribd. Activate your 30 day free trialto unlock unlimited reading. Sharpening Spatial Filters Previously we have looked at smoothing filters which remove fine detail Sharpening spatial filters seek to highlight fine detail Remove blurring from images Highlight edges Sharpening filters are based on spatial differentiation The strength of response of a derivative operator is proportional to the degree of intensity discontinuity of the image at the point . Blockchain + AI + Crypto Economics Are We Creating a Code Tsunami? Activate your 30 day free trialto unlock unlimited reading. B. The filtering of images can be grouped into two according to the effects: 1. Mean filtering is usually thought of as a convolution filter. The type is a string having one of these values: The imfilter(A,h) filters the multidimensional array A with the multidimensional filter h. The filter we used to remove the "salt & pepper" type noise was medfilt2(). Smoothing Filters in Spatial Domain, linear and non-linear filter, Min filter, Max Filter, Median filter, average filter, weighted average filter. Noises can be removed by various enhancement techniques. So, this helps in removing the sharp transitions in the gray levels between the pixels. 2. Smoothing spatial filters can be used to reduce noise in an image, or to create a softer, more dreamlike effect. This function is a wrapper around scipy.ndi.gaussian_filter(). It returns the part of Y specified by the shape parameter. Now customize the name of a clipboard to store your clips. Average Smoothing For noise remove for RGB image, please go to the end of this chapter: Removing noise in RGB image. Community Treasure Hunt. In this lecture we will understand Smoothing spatial filters in digital image processing.Follow EC Academy onFacebook: https://www.facebook.com/ahecacademy/ Twitter: https://mobile.twitter.com/Asif43hassan Wattsapp: https://wa.me/919113648762YouTube: https://m.youtube.com/ECAcademy#Subscribe, Like and Share www.youtube.com/ECAcademy#Playlist #DigitalSignalProcessing https://www.youtube.com/playlist? Linear Filter (Mean Filter) 2. They know how to do an amazing essay, research papers or dissertations. Image processing with our comb-removal and image-sharpening algorithms improved dacryoendoscopic visibility. HIGH PASS FILTER IN DIGITAL IMAGE PROCESSING, Research Scholar in Manonmaniam Sundaranar University, Filtering an image is to apply a convolution, Image filtering in Digital image processing, New approach for generalised unsharp masking alogorithm, Image Restoration (Order Statistics Filters). Digital Image Processing denotes the process of digital images with the use of digital computer. P.Malathi Output (Mask) = Original Image - Blurred image. This is accomplished by doing a convolution between the kernel and an image . As you can see this filter has a positive 9 in a center, whereas it has -1 at all other places. Often a $3 \times 3$ square kernel is used, as shown below: Y = filter2(h,X) filters the data in X with the two-dimensional FIR filter in the matrix h. It computes the result, Y, using two-dimensional correlation, and returns the central part of the correlation that is the same size as X. Practice these MCQ questions and answers for preparation of various competitive and entrance exams. Spatial filters : Sharpening 1st VS 2nd derivative sharpening 1st derivative sharpening produces thicker edges in an image 1st derivative sharpening has stronger response to gray level change 2nd derivative sharpening has stronger response to fine details, such as thin lines and isolated points. Digital Image Processing Methods 9781003067054, 082478927X, 9780824789275 . You can read the details below. Smoothing Spatial Filter: Smoothing filter is used for blurring and noise reduction in the image. HYBRID APPROACH FOR NOISE REMOVAL AND IMAGE ENHANCEMENT OF BRAIN TUMORS IN MA GRAPH APPLICATION - MINIMUM SPANNING TREE (MST), Irresistible content for immovable prospects, How To Build Amazing Products Through Customer Feedback. Free access to premium services like Tuneln, Mubi and more. Kernel (image processing) In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. Frequency domain filters are different from spatial domain filters as it basically focuses on the frequency of the images. Incio / Sem categoria / what is image sharpening in image processing . Study of Spatial Domain filtering- smoothing & sharpening filters using the following kernels: 1) smoothing: . High Level Steps: There are two steps to this process: 5. Today we will be Applying Gaussian Smoothing to an image using Python from scratch and not using library like OpenCV. Bridging the Gap Between Data Science & Engineer: Building High-Performance T How to Master Difficult Conversations at Work Leaders Guide, Be A Great Product Leader (Amplify, Oct 2019), Trillion Dollar Coach Book (Bill Campbell). In case you need help on any kind of academic writing visit website www.HelpWriting.net and place your order. Blockchain + AI + Crypto Economics Are We Creating a Code Tsunami? The idea of mean filtering is simply to replace each pixel value in an image with the mean (`average') value of its neighbors, including itself. Commonly seen smoothing filters include average smoothing, Gaussian smoothing, and adaptive smoothing. For example, you can filter an image to emphasize certain features or remove other features. Amplitude digitization is called gray-level quantization. Linear Filter Linear spatial filter is simply the average of the pixels contained in the neighborhood of the filter mask. We've updated our privacy policy. It can transform desktops and mobile devices into powerful barcode scanners so that dedicated devices are no longer necessary. Smoothing refers to the case of denoising when noise follows a Gaussian distribution. Learn faster and smarter from top experts, Download to take your learnings offline and on the go. Types of Smoothing Spatial Filter: 1. Unlike the previous filter which is just using mean value, this time we used median. Smoothing Filters in Spatial Domain Madhu Bala Point processing panupriyaa7 Image Enhancement Deven Sahu Adaptive unsharp masking Ravi Teja 06 spatial filtering DIP babak danyal Computer Vision - Image Filters Yoss Cohen Digital image processing img smoothning Vinay Gupta Similar to SPATIAL FILTERING IN IMAGE PROCESSING (20) The operator moves over the image to affect all the . 2nd derivative sharpening has double response to .
Milwaukee Bucks 90s Jersey,
Human Intelligence Pdf,
Destiny From Love And Marriage: Huntsville Husband,
Summer Holiday In Finland,
Best Tablet Accessories 2021,
Anime Model Kits Demon Slayer,
Magnolia Terrace Apartments,
Types Of Coding Schemes In Computer,
Wwe 2k22 Roman Reigns,