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Drowsiness and Fatigue of motorists and drivers cause more than 30% of road accidents. Every time, they increase the quantities of deaths, losses, and injuries encyclopedically. Discovery of drowsiness is needed so that it cautions the motorist and the drivers which might save their lives. It's being done using the CNN algorithm in image processing. In this discovery fashion, a motorist is continuously monitored through a webcam. In this technique, the driver's face is taken and it is fed to Deep Learning so that the blinking of the eye is predicted. We use an algorithm, if a motorist's eye seems to be closed for a particular quantum of time, the system cautions the motorist with a sound. To perform the segmentation of the images, ways similar as Image recognition and Image Processing. [ABSTRACT FROM AUTHOR]

Most people who get sick around the world have some kind of skin condition. Despite its frequency, it is very challenging to identify and requires substantial domain experience. This study explains how to identify various types of these disorders. We use a two-stage strategy that efficiently integrates image processing and deep learning based on molecular diagnostic histological features to reliably detect the disease. During the initial stage of the process, the image of the skin condition is processed in a variety of different ways before "feature extraction" is performed. In the second stage of this process, Machine Learning (ML) algorithms are used to diagnose diseases based on histological characteristics discovered through skin examination. These characteristics can be found by analysing the skin. After being trained and evaluated, the system could correctly name up to 95% of the six diseases. [ABSTRACT FROM AUTHOR]

Advancements in deep learning and computer vision have resulted in a surplus of counterfeit facial content that is incredibly realistic in appearance, which is controlled by artificial intelligence., for example, Deep Fake or Face2Face that control facial personalities or on the other hand articulations. Counterfeit appearances made utilizing Generative Adversarial Networks (GANs) are becoming increasingly sophisticated and difficult to distinguish. The synthetic faces were designed primarily for amusement purposes, but their abuse has created emotional harm. For example, a few celebrities have fallen victim to Deep Fake's false erotic entertainment. Concerns are also growing regarding false political debate recordings generated by Face2Face. Building models that can identify fake faces in the media is essential for maintaining personal, societal, political, and international security. The purpose of this paper is to suggest a novel way to identify these counterfeits on the internet using Convolutional Neural Networks (CNN) and image processing techniques. We first used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Complex Shearlet-based edge detection on the dataset and then used the resultant images obtained to train a proposed CNN to categorize fake and real personalities and separate them. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Embedded applications have begun to reap the benefits of embedded vision in recent years. The proliferation of high-performance computers, the maturing of deep learning algorithms, and vision is being added to electronic devices like integrated medical applications due to image processing advances. However, due to the high amount of CPU resources necessary to process even a single image to recognize an object, it is especially challenging to integrate image processing in embedded systems. Deep learning approaches, when deployed and validated on data sets matched to the specific task at hand, may produce better results. We propose and investigate the use of deep learning to improve the image-processing architecture of embedded vision systems in this study. Deep learning algorithm testing on embedded vision yielded encouraging results. [ABSTRACT FROM AUTHOR]

To take out precise ROIs and improve images for precise night-time vehicle number plate detection, we combine a brand-new area of interest (ROI) extraction technique based on improved multi-scale retinex with a method for improving night time images that combines object recommendations with vehicle light detection. (MSR). A suggested score-level feature fusion unifies five complementing traits. The approach we provide can find moving objects that are blurry or partially obscured, as well as moving objects of different shapes, sizes, numbers, positions, and backdrops. The use of a licence plate detection (LPD) system is essential in a variety of traffic-related applications. This project aims to create a sophisticated detecting system that excels in challenging situations. It suggests a reliable preprocessing improvement technique for correctly identifying licence plates in challenging vehicle photos. The suggested approach combines a Gaussian filter with a contrast-limited adaptive histogram equalisation methodology and an enhanced cumulative histogram equalisation method, the local binary pattern and the histogram of an orientated gradient. We identify names using the licence plate. Eventually, we see outcomes. Using the voice command, DON'T PARK IN THIS AREA to send an SMS to the number that is saved in the database. This object has a penalty of this exact amount if it is not obeyed. [ABSTRACT FROM AUTHOR]

This work presents an automated and novel system for cricket commentary generation by the introduction of event driven approach and image captioning features. The presented system uses artificial intelligence and machine learning (ML) based methods to investigate real-time match data and generate real-time commentary by Image Captioning technique. For this purpose, deep neural network-based models and digital image processing techniques are used to detect the significant moments in the real-time match and generate commentary based on these real-time match events. The proposed method has been assessed using a dataset of 2 hours live cricket matches of India and England. After processing the match video, it has been observed that the developed model is successfully able to generate high-quality real-time commentary with a significant amount of accuracy. By commissioning leading-edge deep neural network-based model, the developed model determines the fitness to generate subtitles that are not only precise but also contextually appropriate, and efficiently apprehending the essence of the input frames. [ABSTRACT FROM AUTHOR]

Among all foods, rice is the one that people from different cultures eat the most of. When rice is of high grade, its price rises sharply. As it is, a physical evaluation method using the unaided eye is used to evaluate the rice variety & grade. Nevertheless, this approach is laborious, time-consuming, depends on human expertise, and poses risks to the investigator's wellbeing. This paper proposes a technique that employs computerized methods of image processing to automatically detect and categorise rice grains, therefore addressing the aforementioned problems. This image processing method is ideal because it does not involve any physical touch while photographing the rice grains. To evaluate rice quality, a CNN is utilised to pre-process, partition, & retrieve characteristics from the capturand SVM algorithmic classifiers. Our comparative analysis shows that the suggested system categorization outperforms the alternative. [ABSTRACT FROM AUTHOR]

This research aims to explore the potential of various facial image types in diagnosing Autism Spectrum Disorder (ASD) through the application of deep learning neural networks. It delves into how deep learning algorithms perform with different facial image modalities, especially 2D and 3D, while addressing specific challenges associated with each. The methodology includes training deep learning models on distinct datasets and conducting an in-depth analysis of their accuracy and performance metrics. Significantly, the ResNet50V2 model recorded a 96.9% accuracy rate on the 2D dataset, and the Xception model achieved an 84.4% accuracy rate on the 3D dataset. These findings emphasize the strong capability of deep learning neural networks in making accurate ASD diagnoses from facial images. Nonetheless, the research reveals a stronger proficiency in handling 2D over 3D images, suggesting a need for more comprehensive 3D dataset training to improve three-dimensional image processing. Through evaluating the efficacy of different image modalities, this investigation enriches the field's knowledge base, highlights the necessity for robust dataset development, and charts a course for future studies to advance the precision and practicality of ASD diagnosis via deep learning approaches. [ABSTRACT FROM AUTHOR]

Visual Surveillance is one of the necessary research fields in Computer vision and image processing. Various techniques are developed to perform automated video surveillance. One of the critical challenges in surveillance is detecting abandoned objects in crowded places like parks, shopping complexes and airports, etc., to prevent ecological loss frombomb blasting, gun fires, lost baggage, etc. Closed Circuit Television (CCTV) continuously monitor public places and discriminate unattended and abandoned luggage from different objects. An abandoned thing in surveillance is nothing but an unattended object without a person for a long duration. Identifying these objects in real-time can help prevent bomb blasts and terrorist attacks etc. Many types of research have been performed and published in surveillance for abandoned object detection in recent years. Deep learning is one of the significant fields where various techniques and algorithms are developed for detecting unattended objects. Though these methods detect the objects significantly, they lack computational complexity. A classical edge detection method using Canny edge detection to overcome these challenges is developed to identify the abandoned objects in surveillance scenes. [ABSTRACT FROM AUTHOR]

Deep learning's application to machine learning has seen significant growth in recent years. One of the greatest difficulties now confronting AI systems is the integration of visual data with machine learning in the field of medical diagnostics. Brain tumours, the result of unchecked cell proliferation, are a particularly lethal kind of illness. If painful brain tumours aren't treated properly, they may spread and cause other problems. Cancer treatment relies heavily on an accurate diagnosis. In order to determine whether a tumour is benign or malignant, accurate identification is required. More individuals are diagnosed with cancer now than ever before in part because physicians lack the knowledge to effectively treat tumours at an early stage. In this research, we classify brain MRI and PET scans into tumour and nontumor categories using a convolutional neural network (CNN) technique, augmented by Data Augmentation (DA) and Image Processing (IP) with Image Acquisition (IA). Although we only used a tiny dataset in our experiment, the high level of accuracy (96%) it achieved vouched for the efficiency and minimal complexity of our model. Our approach requires much less computational resources to achieve comparable levels of accuracy as compared to previous pre-trained models. [ABSTRACT FROM AUTHOR]

Tumours are currently the most common cancer cause. Many patients are at risk as a result of cancer. To detect tumours like brain tumours, the medical sector needs a quick, automated, effective, and trustworthy technique. A crucial part of treatment is detection. Doctors keep a patient out of danger if accurate tumour identification is possible. In this application, many image processing methods are applied. Doctors treat many tumour patients properly and save their lives using this application. Currently, medical professionals manually inspect the patient's Magnetic imaging scans of the brain to identify the position and magnitude of the brain neoplasm. This process is time-consuming and results in imprecise detection of the neoplasm. To identify brain tumours, we can apply a Deep Learning framework integrating Convolutional Neural Network (CNN), also called Neural Network, with Visual Geometry Group (VGG) 16 Transfer learning. The advantage of the model is to anticipate the existence of a tumour in an image by returning a positive result if present and a negative effect if not. [ABSTRACT FROM AUTHOR]

Recognizing objects is an important task in many real-world applications, including security systems, surveillance, and robots, making it a hot topic in computer vision research. Because of the progress made in deep learning, real-time object identification systems have been created. These systems can instantly identify things in photos and movies. A major focus of computer vision research, object detection has found practical applications in areas as diverse as autonomous vehicles, robots, security cameras, and people counting. Although deep neural networks have powerful feature representation capabilities in image processing and are often used as feature extraction modules in object detection, their introduction has changed conventional techniques of object identification and detection. Models trained using deep learning may be used as classifiers or regression tools, and they don't need any further human input. This suggests that object identification is a promising area for deep learning research. In order to recognize items in a picture, object detection first needs to pinpoint their exact position (object localization). [ABSTRACT FROM AUTHOR]

Semantic segmentation, once considered a daunting challenge in computer vision, has seen significant advancements with the rise of deep learning. This progress has transformed automated driving from a distant aspiration into a practical reality. However, many existing semantic segmentation algorithms were not specifically designed for autonomous vehicle applications; they were initially developed for more general image processing tasks. In this article, we introduce a reliable technique tailored for semantic segmentation in autonomous driving scenarios. It is imperative to swiftly develop a precise and up-to-date semantic segmentation system to ensure the safe and efficient operation of autonomous vehicles. At the core of this endeavor lies the concept of autonomous driving or self-driving cars. Without the capability to accurately identify and respond to hazards such as pedestrians, other vehicles, and traffic lanes, autonomous vehicles cannot function effectively. [ABSTRACT FROM AUTHOR]

Diabetic patients persist at risk of acquiring Proliferative Diabetic Retinopathy, a retinal condition (PDR). development of neovascularization, a disease in which aberrant blood vessels form on retina, one of key hallmarks of PDR. If not diagnosed & treated early enough, aforementioned illness preserve lead via blindness. Various image processing algorithms considering detecting neovascularization in fundus images have been proposed in a number of studies. Neovascularization, on other hand, difficult via identify due via its erratic growth pattern & tiny size. As a result of their ability via execute automatic feature extraction on objects among complex properties, deep learning approaches persist becoming more common in neovascularization recognition. A fore mentioned research proposes a transfer learning-based approach considering detecting neovascularization. We show certain suggested technique outperforms each individual in detecting neovascularization. [ABSTRACT FROM AUTHOR]

Rice is a vital staple crop, and the presence of diseases can significantly impact its production. Consequently, there is an increasing demand to create efficient strategies for recognizing and controlling diseases in rice plants. This paper provides a comprehensive review of the recent advancements in disease identification techniques for rice plants, focusing on the integration of image processing, machine learning, and deep learning methodologies. Despite the progress made, there remains a need for further research to identify and classify additional rice plant diseases beyond the major ones discussed in this paper. By continually exploring new techniques and expanding the scope of disease identification, researchers have the potential to enhance the overall effectiveness of rice disease detection systems. This review serves as a valuable resource for researchers and practitioners in the field, offering insights into the current state of the art and suggesting future directions for advancements in the identification and management of rice plant diseases introduced. [ABSTRACT FROM AUTHOR]

Various object detection models are presented in this paper. Definition of object is well defined in order to understand object detection models. Now-a-days the object detection which is computer vision and image processing related technology is widely used. Applications of object detections are infinite as it can be used all over like objects tracking, surveillance of videos, face recognition, detection of anomaly, counting of people, and the records still continue to many more. This paper compares various object detection algorithms the basic splitting is based on machine learning based and deep learning based. Fundamental objective of the paper focuseson Single stage object detectors (SSD) like You Only Look Once (YOLO) and Two stage object detectors as RCNN, Fast RCNN, etc. This paper's main goal is to highlight such object detection models and their use in various fields. [ABSTRACT FROM AUTHOR]

Skin Cancer is one of the most common types of disease in the world, including Indonesia. This disease is caused by food that can be toxic to the human body and the effects of global warming. Especially melanoma, this type of skin cancer that occurs in melanocyte cells is a very serious threat. In the United States, although melanoma is only 5% of all cases of skin cancer that occur, melanoma has been the cause of death in 75% cases of death from skin cancer in the country. Melanoma has a similar shape as mole, birthmark or beauty sign that is often ignored. To recognize the presence of melanoma early on, this study uses the method of deep convolutional neural network to classify skin cancers that occur in melanocytes, where melanoma grows. The deep learning method used has good accuracy and good use in image recognition. Dermoscopy image of skin cancer is used as input for image processing. Before entering the classification stage, the image is processed with the image processing Scaling and contrast enhancement. After testing phase in this study, it concludes that the proposed method has the ability to classify skin cancer that occurs in melanocytes quite well with an accuracy of 93.3%. [ABSTRACT FROM AUTHOR]

In the face of the upcoming Corona pandemic in 2020, which continues to this day, adequate protection for all citizens has been very important and continues to be essential. An easy way to minimize the risk of infection in public, but poorly ventilated places, are FFP masks. The basic material from which FFP masks are made is nonwoven. The quality of the produced nonwoven is the basis for the correct function and thus the protection against infection. The present research work investigates methods of computer vision to detect contaminations and damages on the nonwoven. Both supervised and semi supervised methods are evaluated. For this work, an inspection system consisting of two separate acquisition systems was developed for image data acquisition. The first system is suitable for the visible wavelength and has a theoretical resolution of 2.4 µm per pixel. The second camera system is designed for the near infrared range and has a theoretical resolution of 5.5 µm per pixel. The acquisition system collects an image data set comprising 1,760 images with 920 images of defect-free nonwoven samples and 840 images of defective nonwoven samples. First, the wavelength range suitable for optical inspection of uncoated nonwoven samples is investigated. A further investigation dealt with the question of whether the reflected light or transmitted light method is more suitable for optical inspection. Finally, coated nonwoven is also inspected using the reflected light method. An investigation using the transmitted light method is not possible, as the material is almost non-transparent. Despite the small amount of data, very good results were achieved. Machine learning methods from the field of image processing are usually classified as deep learning. This means that the large network architectures require very large amounts of data in order to learn complex patterns. Publicly available datasets for method evaluation typically consist of over 1,000 images per class or defect. The nonwoven samples provided in this work and the resulting image database is about a factor of six smaller than is actually intended for the methods used. In this work it has been possible to achieve inspection accuracies of 97.5 %. [ABSTRACT FROM AUTHOR]

Early diagnosis of brain tumors depends on their detection and treatment of brain-related disorders. When it comes to diagnosing brain tumors, for example, deep learning models have shown remarkable success in the field of medical picture processing in recent years. The implementation of two cutting-edge convolutional neural network designs, DenseNet169 and DenseNet201, for brain tumor identification is the main emphasis of this study. A sizable dataset of magnetic resonance imaging (MRI) scans, including different types and sizes of brain tumors, was used to train the DenseNet169 and DenseNet201 models. These models' extensive connections enable data exchange and feature reuse, improving performance and enhancing tumor localization accuracy. The trained models are assessed using a different testing dataset, and their performance indicators, such as accuracy and loss, are examined. Both models' effectiveness is shown by the findings. DenseNet201 outperformed Dense 169 ResNet 50 & VGG19, with test accuracy of 88.58, train accuracy of 95.98, train loss of 10.36, and test loss of 33.59. [ABSTRACT FROM AUTHOR]

The deep learning technology is growing on day by day with increasing the internet access. The techniques of deep learning used to process information as human brain activities like learning some knowledge, solving the problems, and making the decision. Generally, deep learning provides self.training. In deep learning, CNN architecture is commonly used in many areas such as image processing, video processing, speech recognition, natural language processing etc. In the paper, the overview of deep learning is represented in which artificial intelligence, machine learning deep neural network are introduced. After that, layers of CNN architecture with its layers and techniques of deep learning are discussed. The CNN architecture is also used in image processing for security purposes. [ABSTRACT FROM AUTHOR]

Pneumonia is a common respiratory ailment impacting millions worldwide. It stems from bacterial, viral, or fungal infections and can escalate into a serious, life-threatening condition if not promptly addressed. As per the World Health Organization (WHO), pneumonia stands as the leading cause of death among children under five globally, contributing to nearly 15% of all fatalities in this age group. Moreover, pneumonia accounts for 15% of all hospitalizations in theUnited States and is a prominent cause of death globally, especially in low-income nations. Pneumonia diagnosis must be preciseand prompt for optimal treatment and patient outcomes. Deep learning algorithms have developed as a useful tool for analyzing and classifying medical pictures, especially chest X-rays used to detect pneumonia, in recent years. The correct categorization of pneumonia into several categories can assist healthcare providers in making educated decisions about patient care and treatment. We can increase the accuracy and efficiency of pneumonia diagnosis using deep learning approaches, potentially leading to betterpatient outcomes. The study emphasizes the promise of deep learning techniques in medical image processing, as well as their ability to enhance healthcare outcomes. [ABSTRACT FROM AUTHOR]

Coffee is an important international commodity, especially for Indonesia, which ranks fourth as the world's coffee producer. However, in the field, coffee plants are susceptible to pests and diseases, which can reduce the yield and quality of the finished product. For farmers who often rely on rain-fed crops and have limited access to financial and technical support, this is a significant obstacle. Image processing and machine learning techniques, particularly deep learning and convolutional neural networks (CNN), can help identify and early detect plant diseases. This study used a quadcopter drone for image acquisition and employed CNN architectures, specifically AlexNet with 50, 75, and 100 epochs, to classify coffee leaves into three classes: healthy, leaf rust, and leaf spot. The best results were achieved by Alexnet with 100 epochs with an accuracy of 93.7%, precision of 98%, and recall of 100%. Thus, it can be concluded that the CNN method using the Alexnet model with 100 epochs and combined with a quadcopter drone can be used to detect coffee plant diseases. [ABSTRACT FROM AUTHOR]

Foreign species, often known as invasive species, are frequently introduced to native species via any means, including the air, birds, and insects. This could have a negative impact on the local environment. Only a few of the negative effects of invasive plants in natural environments include reduced native biodiversity, changing species composition, habitat loss for dependent and native species, altered biogeochemical cycles, and altered disturbance regimes. There have been a few recommendations in previous articles to prevent this, but we propose a fresh technique to dealing with this issue using convolutional neural networks and image processing in this study. We initially take sample images of each flower species in the area in order to construct a dataset of all native flower species. Then, using the image dataset, train a deep learning model to identify the species. It is critical to identify any outlier or abnormal flower species observed in order for a skilled botanist to analyses the plant and determine whether it is detrimental to the park's ecology. The Deep Learning model's output will be communicated to the concerned party via the warning mechanism. This may be accomplished by using the Signal app bot, which connects to a certain API and then sends the message to the receiver. [ABSTRACT FROM AUTHOR]

The development of computer vision technology has made it simpler than ever to learn and use sign languages to converse with those who are deaf and mute. Exciting research is being done on a global platform for sign language communication. Deep learning and images are used to provide a mechanism for understanding signs in both Indian Sign Language and American Sign Language. The computer programme can anticipate the user's A-Z alphabet signals. The Convolutional Neural Network's training time and storage requirements are considerably reduced when RGB, GREEN, and BLUE data are converted to grayscale images by image processing. The experiment's goal is to find the best image manipulation and deep learning architecture to execute for the system's mobile applications. Networks are used to teach the database from the ground up Vgg16, Inception-v3, and MobileNet-v2. The report includes a comparison of predicted accuracies. The finalised Inception-v3 network improved training accuracy for American Sign Language to 98.3% and for Indian Sign Language to 99.8%, and also increased validation accuracy to 99.5% and 99.7%, respectively. [ABSTRACT FROM AUTHOR]

Identification of sickle cell disease (SCD) plays a crucial role in healthcare image analysis. By categorizing the red blood cell abnormalities and cell counts, it comprehensively analyzes the illness and provides an accurate identification. The diagnosis of SCD severity for the diagnostic procedure is dependent significantly on the detection process. Cell morphology performs an important role in distinguishing between normal and sickle cells. The analysis of the cells is complicated by the existence of overlapping cells in the images. The proper segmentation of overlapping cells is essential for SCD identification. This study examines how traditional image processing and a deep learning model separate overlapping cell. Before segmentation, images have been enhanced and pre-processed to ensure that the overlapped cells are accurately separated. We evaluate the deep learning model of the Star Convex Polygon method (SCP) with pre-processed images and raw images to investigate how the image pre-processing technique affects overlap cell segmentation. We analyzed 80 images to test and achieved 97.96% accuracy for overlap cell segmentation using the SCP model and pre-processed images. [ABSTRACT FROM AUTHOR]

The Corona virus wreaked havoc on the worldwide health system. The transmission of Corona virus can be reduced and prevented by wearing a face mask. To eradicate the spreading of Corona virus among the individuals we proposed a system that can detect the individual who are not wearing mask and alert the individual and we employed a dataset with and without masks photos from the public domain Kaggle facial mask detection dataset, and a predefined model MobileNetV2 and CNN Algorithm are accomplished for real-time face detection. We use Python, OpenCV, Tensor Flow, and Keras to develop a facemask detector in depth using computer vision techniques. Our objective is to apply image processing techniques and deep learning algorithms to analyse an image or video stream to ascertain whether an individual is wearing a face mask or not. If the person is not, an alarm warning will be generated. In proposed model Swish activation function applied in place of ReLu to generate an optimal accuracy of 99.81 [ABSTRACT FROM AUTHOR]

Due to the overuse of antibiotics, antibiotic resistance in bacteria has emerged as a significant public health concern. Various methods can be employed to determine the susceptibility of bacteria to antibacterial compounds, and artificial intelligence (AI) has proven to be highly impactful in this field, especially with the advancements in image processing and classification. This research investigates two approaches for classifying antibiotic-resistant bacteria: traditional machine learning (ML) using the Orange Data Mining tool and deep learning with Convolution Neural Network (CNN). Using an antibiotic disk image dataset, various classifier models are compared using the Orange Data Mining tool. The traditional classifier model Logistic Regression is found to be the most accurate with a 91% accuracy rate, followed by Neural Networks (NN) with 90% accuracy. Other techniques such as Support Vector Machine (SVM), k-nearest neighbors (KNN), and decision trees (DT) are also analyzed, with varying levels of accuracy. Deep learning with CNN is found to be more efficient in classification, achieving an impressive 99.99% accuracy. This study's high accuracy results suggest that CNN could be a promising tool for the rapid and reliable diagnosis of antibiotic-resistant bacteria in the future. [ABSTRACT FROM AUTHOR]

Image restoration is the process in which a corrupted image is restored to its original quality. Since there are plenty of computer vision applications where images are widely used and this is very important to restore noisy or corrupted images for high visual quality and accuracy of image processing-based applications. We aim at providing multiple techniques that caters to the needs of images of varied quality. The techniques to be implemented in this project for image restoration include different deep learning approaches such as SRCNN, SRGAN, deep learning attributes, Ffmpeg (Fast Forward Moving Pictures Experts Group), and bilateral filter. A unified method is given for various types of inputs as it can repair the damaged images and enhance. Super resolution, dehazing, colorization and inpainting are the blocks through which the input go through and get an escalated output. It can support grayscale and colored images. The application is built using Python data science platform. Enables users to give any image as input and output can be seen on the same screen. For human eye perception, sometimes, output and input may appear same. In order to show the quality improvement, we use statistical measures such as PSNR (Peak signal to noise ratio) and SSIM (Structural similarity index measure). These metrics show the performance improvement with quantitative results for proof of the concept. This project also has provision to enhance short videos clips. As the main goal is refinement. [ABSTRACT FROM AUTHOR]