IndexIntroductionLiterature ReviewResearch FrameworkNoise ReductionContrast EnhancementDenoisingAdvanced VideoMethodologyFourier TransformConclusionVideo enhancement is one of the most important and difficult components in video research. The purpose of video enhancement is to improve the visual appearance of the video or provide a better transformed representation for future automated video processing, such as analysis, detection, identification, recognition, surveillance, traffic, criminal justice systems. Generally we will see the noise in the old days when we recorded the video in the VCR where there will be the red, green and blue color dots which are the noise in the video. We will eliminate them with video enhancement techniques. In today's world, image and video enhancement techniques are very important. Here, using image and video enhancement techniques, we will improve the quality of the image and videos so that you can get a better image. Many images such as medical images, satellite images, aerial images, and even real-life photographs suffer from poor contrast and noise. It is important to improve contrast and remove noise to increase image quality. One of the most important steps in medical image detection and analysis are image enhancement techniques that improve the quality (clarity) of images for human vision, remove blur and noise, increase contrast and reveal details are examples of improvement operations. In practice we eliminate the noises and disturbances that occurred while taking the image. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Introduction Image processing is a strategy for performing some operations. to get an improved image. It is a type of signal processing where the input is an image and the output will also be an image but the output image will be the clear one without noise and disturbance. Nowadays, image processing and video enhancement are among the rapidly developing innovations. It frames the central research area within the Retinex strategy which essentially includes two phases: illumination estimation and normalization. Step-by-step instructions for precisely untangling background lighting are a key issue. The backgrounds of the image arrangement in the near edges of the video are usually comparative and firmly related. More precise lighting information can be extracted when considering these attributes of the video photo sequence. Retinex improves the visual performance of an image when lighting conditions are poor. While our eye can see nuances effectively in low light, cameras and camcorders cannot handle this effectively. The MSRCR i. and MultiScale Retinex with Color Restoration, the computation, which underlies the Retinex channel, is motivated by the eye's natural components to adapt to these conditions. Retinex remains for Retina + Cortex. A gray level modification strategy that allows us to improve the contrast of the image and also improve the homogeneity of the image areas. It depends on the optimal classification of the gray levels of the image, followed by a close parametric change of the gray level connected to the obtained classes. With the method for two parameters that separately talk about a homogenization coefficient (r) and a desired number (n) of classes in the performance image, grayscale modification techniques (called gray level scaling) have aplaced in the classification of point operations and capabilities by changing the pixel values ​​(gray level) using a mapping equation. The mapping equation is normally direct (nonlinear conditions can be visualized by piecewise linear transformation and maps the original gray level qualities to other indicated values. Regular applications incorporate contrast enhancement and feature enhancement. The essential operations related to the dark dimension of an image are to pack or extend it. We normally package ranges of gray levels that we are not very excited about and extend the gray levels where we are looking for more data , this is called gray level compression, while if the skew is greater than one, it is called gray level stretching. The first edited images where we can see that extending this range has uncovered previously hidden visual data From time to time we may need to extend a particular scope of gray levels, while reducing the qualities to a minimum. Now, to make a noise-free video, we will add snapshots of the infinite loop images that are eliminated using the above filters and the output video will be noise-free and adjustments will be made to contrast and noise cancellation. Literature Review Real-time video updating is generally achieved using exorbitant particular equipment with specific capacities and outputs. Standard enterprise equipment, such as desktop PCs with graphics processing units (GPUs), are also typically used as cost-effective solutions for continuous video management. Previously, the limitations of PC equipment meant that constant video enhancement was essentially done on desktop GPUs with insignificant Central Processing Unit (CPU) usage. These computations were basic in nature and effectively parallelizable, which allowed them to carry out continuous execution. However, even complex enhancement calculations require subsequent data preparation, and this cannot be done effectively and progressively on a GPU. In this article, current advances in portable CPU and GPU equipment are used to perform recent video enhancement calculations on a versatile PC. Both the CPU and GPU are adequately utilized to perform real-time execution of complex image enhancement calculations that require both consecutive and parallel handling operations. Results for histogram adjustment, versatile histogram balancing, contrast enhancement using tone mapping, and combining presentation of several 8-bit scaled-down recordings up to 1600x1200 pixels in size are shown. Unfavorable weather conditions such as snow, fog or overwhelming precipitation incredibly reduce the visual quality of outdoor observation recordings. Upgrading the video quality can improve the visual quality of reconnaissance recordings by providing clearer images and more subtle elements. Existing work in this area focuses primarily on improving quality for high determination recordings or still images, but little calculation is made to improve reconnaissance recordings, which normally have low determination, high noise, and high resolution antiques. pressure. Additionally, in snowy or rainy conditions, the image quality of the near-field perspective is degraded by the darkening of obvious snowflakes and raindrops, while the qualityof the far-field perspective image is degraded by the darkening of fog-like snowflakes or raindrops. Not many video quality improvement calculations have been developed to handle both problems. Search Box Low light video is linked to the initial stage which is pre-processing. Image preprocessing is the name of operations on images at the minimum level of reflection whose purpose is a change of image information that drowns out unwanted mutilations or improves some image highlights indispensable for further preparation. It does not create image data content. His techniques utilize the vast excess of images. Noise Reduction Noise is generally the result of errors that occur in image acquisition. Which results in pixel values ​​that don't reflect the actual scene. variety of noises and various noise reduction strategies classified into two domains: spatial domain and frequency domain. Enhancing contrastContrast is defined as the separation between the darkest and brightest areas of the image. Increasing the contrast also increases the separation between dark and light, making shadows darker and highlights brighter. Adding contrast usually adds "pop" and makes the image more vibrant, while decreasing contrast can make the image duller. An image's contrast is a measure of its dynamic range, or the "spread" of its histogram. for light video enhancement we need to apply filtering techniques to attenuate the remaining noise. Most noise is removed by noise reduction techniques, noise is introduced by the contrast enhancement step. Noise reduction is performed using various filters. Advanced Video The output video will be noise-free and the contrast and noise cancellation will be adjusted. We finally get an improved video. MethodologyMATLAB provides the functionality needed for basic video processing using short video clips and a limited number of video formats. Not long ago, the only video container supported by built-in MATLAB functions was the AVI container, through functions like aviread, avifile, movie2avi, and avi info. We will take an original video file as inputaviread: it reads an AVI movie and stores its frames in a MATLAB movie structure. aviinfo: Returns a structure whose fields contain information (for example, frame width and height, total number of frames, frame rate, file size, etc.) about the AVI file passed as a parameter. Mmreader: Constructs a media player object that can read video data from a variety of media file formats. The video is divided into individual snapshots. Convert frame to image using frame2im. Process the image using any technique. Convert the result back to a frame using im2frame. Here we are using a continuous frame of images. If we have R, G, B values ​​we are using the image enhancement function. If it is a black and white image we are using grayscale enhancement using 0 and 1GrayscaleIn photography and computing, a grayscale digital image is an image in which the value of each pixel is a single sample, that is, it only carries intensity information. Images of this type, also known as black and white, are composed exclusively of shades of gray, ranging from black with the weakest intensity to white with the strongest intensity. Grayscale images are distinct from single-bit black-and-white bitonal images. white images, which in the context of computer imaging are images with only two colors, white andblack (also called bit-level images or binary images). Grayscale images have many shades of gray in between. Grayscale images are often the result of measuring the intensity of light on each pixel in a single band of the electromagnetic spectrum (e.g. infrared, visible light, ultraviolet, etc.) and in these cases are true monochrome when only a certain frequency is captured. But they can also be synthesized from a color image; see the section on converting to grayscale. Numerical representation: the intensity of a pixel is expressed within a given interval between a minimum and a maximum inclusive. This interval is abstractly represented as a range between 0 (total absence, black) and 1 (total presence, white), with any fractional values ​​in between. This notation is used in academic articles, but does not define what "black" or "white" is in terms of colorimetry. Another convention is to use percentages, so the scale goes from 0% to 100%. This is used for a more intuitive approach, but if only integer values ​​are used, the range comprises a total of only 101 intensities, which is not enough to represent a large gray gradient. Also, percentile notation is used in printing to indicate the amount of ink used in halftones, but then the scale is reversed, 0% being white paper (no ink) and 100% a solid black (full ink). In computing, although grayscale can be calculated using rational numbers, image pixels are stored in binary and quantized form. Some early grayscale monitors can only display up to sixteen (4-bit) different shades, but today grayscale images (such as photographs) intended for visual display (whether on-screen or printed) are commonly stored with 8-bit per sampled pixel, which allows 256 different intensities (i.e. shades of grey) to be recorded, typically on a non-linear scale. The precision provided by this format is just enough to avoid visible banding artifacts, but very convenient for programming because a single pixel therefore occupies a single byte. Technical uses (e.g. in medical imaging or remote sensing applications) often require multiple levels, to make full use of the sensor's precision (typically 10 or 12 bits per sample) and to avoid rounding errors in calculations. Sixteen bits per sample (65,536 levels) is a convenient choice for such uses, since computers efficiently handle 16-bit words. The TIFF and PNG image file formats (among others) natively support 16-bit grayscale, although browsers and many imaging programs tend to ignore the low-order 8 bits of each pixel. Regardless of the pixel depth used, binary representations assume that 0 is black and the maximum value (255 at 8 bpp, 65, 535 at 16 bpp, etc.) is white, unless otherwise specified. F. We will enhance each image by improving contrast and removing noiseG. If it is a color image, we will divide the current image into R, G and B values ​​since the original image will be a combination of all three H values. We will apply the inverse and Fourier transform to each R value, G, B. Fourier transformsA transformation is a mathematical tool that allows the conversion of a set of values ​​into another set of values, thus creating a new way of representing the same information. In the field of image processing, the original domain is called the spatial domain, while the results are in the transformation domain. The motivation for using transformations.