Table of ContentsIntroductionLiterature ReviewResearch FrameworkNoise ReductionContrast EnhancementDenoisingVideo EnhancedMethodologyFourier TransformsConclusionVideo enhancement is one of the most important and challenging components of video search. The goal 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, trafficking and criminal justice systems. Generally we will see the disturbances in the ancient times when we recorded the video in the VCR in which there were the red, green and blue colored dots which are the disturbances in the video. We will erase 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 image and videos to get a better picture. Many images such as medical images, satellite images, aerial images, and even real photographs suffer from low contrast and noise. It is important to improve contrast and remove noise to increase image quality. Among the most essential steps in medical image detection and analysis are image enhancement techniques that improve the quality (clarity) of images for human viewing, remove blur and noise, increase the contrast and reveal details are examples of enhancement operations. We essentially eliminate the noise and disruption that occurs when shooting. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essayIntroductionImage processing is a strategy for performing a few operations. in order to obtain an improved image. This is a type of signal processing in which the input is an image and the output will also be an image, but the output image will be clear without any noise or disturbance. Nowadays, image processing and video enhancement are among the rapidly developing innovations. It frames the central research area within the Retinex strategy and essentially includes two stages: estimation and standardization of lighting. Step-by-step instructions for accurately extracting background lighting is a key issue. The backgrounds of the arrangement of images in the near edges of the video are generally comparative and closely related. More accurate lighting information can be extracted when these attributes of the video's photo sequence are taken into account. Retinex improves the visual rendering of an image when lighting conditions are poor. Although our eye can see hues effectively in low light, stills and video cameras cannot handle this well. The MSRCR i. The MultiScale Retinex calculation with color restoration, which is the basis of the Retinex channel, is driven by the natural components of the eye to adapt to these conditions. Retinex remains for Retina + cortex. A strategy for modifying the gray levels which makes it possible to improve the contrast of the image and in addition to improve the homogeneity of the areas of the image. It depends on the optimal classification of the gray levels of the image, followed by a close parametric gray level change related to the obtained classes. By method for two parameters speaking, separately, a homogenization coefficient (r)and a desired number (n) of classes in the yield image, gray scale modification techniques (called grayscale scaling) have their place in classifying point operations and capacity by modifying pixel values ​​(gray level) by a mapping equation. The mapping equation is generally simple (nonlinear conditions can be displayed by piecewise linear transformation and maps the original grayscale qualities to other specified values. Regular applications incorporate contrast enhancement and l Improving functionality The essential operations related to the dark size of an image are packing or extending it. We generally pack grayscale ranges that we are not interested in and extend the grayscales where we are looking for more data. If the skew of the line is between zero and one, it is called grayscale compression, while if the skew is greater than one, it is called grayscale stretching The first frames. edited where it can be seen that extending this range has revealed previously hidden visual data. From time to time we may need to extend a particular grayscale range, while reducing the qualities to the lowest. Now to create a noise free video we will add the infinite loop frame snapshots which are cleared using the above filters and the output video will be disturbance free and there will be contrast adjustments and deletions of noise. Literature Review Real-time video upgrading is usually accomplished using exorbitantly specific equipment that has specific capabilities and efficiencies. Off-the-shelf professional equipment, for example desktop computers with graphics processing units (GPUs), are also commonly used as financial solutions for continuous video processing. Previously, lockdowns in PC hardware meant that constant video enhancement was done primarily on desktop GPUs with insignificant central processing unit (CPU) usage. These calculations were basic in nature and effectively parallelizable, allowing them to perform continuous execution. Be that as it may, complex improvement calculations also require the successive preparation of information, which cannot be achieved efficiently and gradually on a GPU. In this article, the current advancements in portable CPU and GPU equipment are used to run video enhancement calculations recently on a general-purpose PC. The CPU and GPU are adequately utilized to achieve real-time execution of complex image enhancement calculations that require both consecutive and parallel processing operations. Results are presented for histogram equalization, general-purpose local histogram balancing, contrast enhancement using tone mapping, and combining presentation of different 8-bit downscaled recordings. with a size of up to 1600 x 1200 pixels. Adverse weather conditions such as snow, mist or heavy precipitation significantly decrease the visual quality of outdoor observation recordings. Upgrading video quality can improve the visual quality of reconnaissance recordings by giving clearer images and more subtle elements. Existing work in this area mostly focuses on improving the quality of high determination recordings or still images, but few calculations are created to improve reconnaissance recordings, which normally have lowdetermination, high noise and pressure. Additionally, in snow or rain, the close-field perspective image quality is degraded by the obscuration of obvious snowflakes and raindrops, while the far-field perspective quality is degraded by the obscuring of snowflakes or raindrops resembling fog. Few video quality enhancement calculations have been developed to address these two issues. Research Framework Low light video is connected to the initial stage which is preprocessing. Image pre-processing is the name for operations on images at the most minimal level of reflection whose goal is a change in image information that suppresses unwanted mutilations or enhances certain image highlights essential to further preparation. It does not create image data content. His techniques use considerable excess of images. Noise Reduction Typically, noise is the result of errors that occur during image acquisition. Which results in pixel values ​​that do not reflect the actual scene. variety of noise types and various noise reduction strategies which are classified into two domains which are spatial domain and frequency domain. Contrast EnhancementContrast is defined as the separation between the darkest and brightest areas of the image. Increase the contrast and you increase the separation between dark and light, making shadows darker and highlights brighter. Adding contrast generally adds "pop" and makes an image more vibrant, while decreasing contrast can make an image duller. An image's contrast is a measure of its dynamic range, or the "extent" of its histogram. Denoising The final stage of low light video enhancement, we need to apply filtering techniques to smooth out the remaining noise. Most of the noise is removed by noise reduction techniques, noise is introduced by a contrast enhancement stage. Denoising is done using various filters. Enhanced Video The video output will be free of disturbances and there will be contrast adjustments and noise removals. Finally, we get an enhanced 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 MATLAB's built-in functions was the AVI container, through functions such as aviread, avifile, movie2avi, and avi info. We will take an original video file as inputaviread: plays an AVI movie and stores it. images in a MATLAB movie structure. aviinfo: returns a structure whose fields contain information (for example, image width and height, total number of images, frame rate, file size, etc.) about the AVI file passed as a parameter. Mmreader: Constructs a media player object capable of reading 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 frame using im2frame.Here we use continuous frame of images.If we have R,G,B values ​​then we use image enhance function.If it s This is a black and white image, we use a gray scale. enhancement using 0 and 1GrayscaleIn photography and computing, a grayscale or grayscale digital image is one in which the value of each pixel is a single sample, that is, it contains only intensity information. THEImages of this type, also known as black and white, are composed exclusively of shades of gray, ranging from black at the lowest intensity to white at the strongest intensity. Grayscale images are distinguished from one-bit dual-tonal black and white. white images, which in the context of computer imaging are images with only two colors, black and white (also called bitwise or binary images). Grayscale images have many shades of gray in between. Grayscale images are often the result of measuring the intensity of light at each pixel in a single band of the electromagnetic spectrum (e.g. infrared, visible light, ultraviolet, etc.), and in such case they are properly monochromatic when only a given frequency is captured. But they can also be synthesized from a color image; see the section on grayscale conversion.Numerical representation: The intensity of a pixel is expressed in a given range between a minimum and a maximum inclusive. This range is represented abstractly as a range from 0 (total absence, black) to 1 (total presence, white), with all fractional values ​​in between. This notation is used in academic articles, but it 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 encompasses a total of only 101 intensities, which is insufficient to represent a broad gray gradient. Additionally, percentile notation is used in printing to indicate the amount of ink used in halftone, but the scale is then reversed, i.e. 0% white of the paper (no ink) and 100% of solid black (full ink). In computing, although gray levels can be calculated using rational numbers, image pixels are stored in binary and quantized form. Some early grayscale monitors could only display up to sixteen (4-bit) different shades, but today grayscale images (such as photographs) intended for visual display (both on screen and printed) are typically stored with 8 bits per sampled pixel, allowing 256 different intensities (i.e. shades of gray) to be recorded, typically on a non-linear scale. The precision provided by this format is barely sufficient to avoid visible banding artifacts, but very practical for programming because a single pixel then occupies a single byte. Technical uses (e.g. in medical imaging or remote sensing applications) often require more levels, to utilize sensor precision (typically 10 or 12 bits per sample) and to guard against rounding errors in the calculations. Sixteen bits per sample (65,536 levels) is a practical choice for such uses because computers handle 16-bit words efficiently. 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 0 is black and the maximum value (255 at 8 bpp, 65, 535 at 16 bpp, etc.) is white unless otherwise noted. F. We will enhance each image by enhancing contrast and removing noiseG. If it is a color image, we will divide the current image into R, G and B values ​​because the original image will be a combination of all three.