110 skills found · Page 1 of 4
DmitryUlyanov / Deep Image PriorImage restoration with neural networks but without learning.
braindotai / Watermark Removal Pytorch🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
cszn / DPIRPlug-and-Play Image Restoration with Deep Denoiser Prior (IEEE TPAMI 2021) (PyTorch)
cszn / IRCNNLearning Deep CNN Denoiser Prior for Image Restoration (CVPR, 2017) (Matlab)
yossigandelsman / DoubleDIPOfficial implementation of the paper "Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors"
atiyo / Deep Image PriorImage reconstruction done with untrained neural networks.
ricedsp / D AMP ToolboxThis package contains the code to run Learned D-AMP, D-AMP, D-VAMP, D-prGAMP, and DnCNN algorithms. It also includes code to train Learned D-AMP, DnCNN, and Deep Image Prior U-net using the SURE loss.
liufeng2317 / ADFWIAn Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
saeed-anwar / UWCNNCode and Datasets for "Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement", Pattern Recognition, 2019
YunChunChen / NAS DIP Pytorch[ECCV 2020] NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
junjun-jiang / SSPSRA spatial-spectral prior deep network for single hyperspectral image super-resolution (IEEE TCI)
prs-eth / ResDepth[ISPRS Journal of Photogrammetry and Remote Sensing, 2022] ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images
bookworm52 / EthicalHackingFromScratchWelcome to my comprehensive course on python programming and ethical hacking. The course assumes you have NO prior knowledge in any of these topics, and by the end of it you'll be at a high intermediate level being able to combine both of these skills to write python programs to hack into computer systems exactly the same way that black hat hackers do. That's not all, you'll also be able to use the programming skills you learn to write any program even if it has nothing to do with hacking. This course is highly practical but it won't neglect the theory, we'll start with basics of ethical hacking and python programming and installing the needed software. Then we'll dive and start programming straight away. You'll learn everything by example, by writing useful hacking programs, no boring dry programming lectures. The course is divided into a number of sections, each aims to achieve a specific goal, the goal is usually to hack into a certain system! We'll start by learning how this system work and its weaknesses, then you'll lean how to write a python program to exploit these weaknesses and hack the system. As we write the program I will teach you python programming from scratch covering one topic at a time. By the end of the course you're going to have a number of ethical hacking programs written by yourself (see below) from backdoors, keyloggers, credential harvesters, network hacking tools, website hacking tools and the list goes on. You'll also have a deep understanding on how computer systems work, how to model problems, design an algorithm to solve problems and implement the solution using python. As mentioned in this course you will learn both ethical hacking and programming at the same time, here are some of the topics that will be covered in the course: Programming topics: Writing programs for python 2 and 3. Using modules and libraries. Variables, types ...etc. Handling user input. Reading and writing files. Functions. Loops. Data structures. Regex. Desiccation making. Recursion. Threading. Object oriented programming. Packet manipulation using scapy. Netfilterqueue. Socket programming. String manipulation. Exceptions. Serialisation. Compiling programs to binary executables. Sending & receiving HTTP requests. Parsing HTML. + more! Hacking topics: Basics of network hacking / penetration testing. Changing MAC address & bypassing filtering. Network mapping. ARP Spoofing - redirect the flow of packets in a network. DNS Spoofing - redirect requests from one website to another. Spying on any client connected to the network - see usernames, passwords, visited urls ....etc. Inject code in pages loaded by any computer connected to the same network. Replace files on the fly as they get downloaded by any computer on the same network. Detect ARP spoofing attacks. Bypass HTTPS. Create malware for Windows, OS X and Linux. Create trojans for Windows, OS X and Linux. Hack Windows, OS X and Linux using custom backdoor. Bypass Anti-Virus programs. Use fake login prompt to steal credentials. Display fake updates. Use own keylogger to spy on everything typed on a Windows & Linux. Learn the basics of website hacking / penetration testing. Discover subdomains. Discover hidden files and directories in a website. Run wordlist attacks to guess login information. Discover and exploit XSS vulnerabilities. Discover weaknesses in websites using own vulnerability scanner. Programs you'll build in this course: You'll learn all the above by implementing the following hacking programs mac_changer - changes MAC Address to anything we want. network_scanner - scans network and discovers the IP and MAC address of all connected clients. arp_spoofer - runs an arp spoofing attack to redirect the flow of packets in the network allowing us to intercept data. packet_sniffer - filters intercepted data and shows usernames, passwords, visited links ....etc dns_spoofer - redirects DNS requests, eg: redirects requests to from one domain to another. file_interceptor - replaces intercepted files with any file we want. code_injector - injects code in intercepted HTML pages. arpspoof_detector - detects ARP spoofing attacks. execute_command payload - executes a system command on the computer it gets executed on. execute_and_report payload - executes a system command and reports result via email. download_and_execute payload - downloads a file and executes it on target system. download_execute_and_report payload - downloads a file, executes it, and reports result by email. reverse_backdoor - gives remote control over the system it gets executed on, allows us to Access file system. Execute system commands. Download & upload files keylogger - records key-strikes and sends them to us by email. crawler - discovers hidden paths on a target website. discover_subdomains - discovers subdomains on target website. spider - maps the whole target website and discovers all files, directories and links. guess_login - runs a wordlist attack to guess login information. vulnerability_scanner - scans a target website for weaknesses and produces a report with all findings. As you build the above you'll learn: Setting up a penetration testing lab to practice hacking safely. Installing Kali Linux and Windows as virtual machines inside ANY operating system. Linux Basics. Linux terminal basics. How networks work. How clients communicate in a network. Address Resolution Protocol - ARP. Network layers. Domain Name System - DNS. Hypertext Transfer Protocol - HTTP. HTTPS. How anti-virus programs work. Sockets. Connecting devices over TCP. Transferring data over TCP. How website work. GET & POST requests. And more! By the end of the course you're going to have programming skills to write any program even if it has nothing to do with hacking, but you'll learn programming by programming hacking tools! With this course you'll get 24/7 support, so if you have any questions you can post them in the Q&A section and we'll respond to you within 15 hours. Notes: This course is created for educational purposes only and all the attacks are launched in my own lab or against devices that I have permission to test. This course is totally a product of Zaid Sabih & zSecurity, no other organisation is associated with it or a certification exam. Although, you will receive a Course Completion Certification from Udemy, apart from that NO OTHER ORGANISATION IS INVOLVED. What you’ll learn 170+ videos on Python programming & ethical hacking Install hacking lab & needed software (on Windows, OS X and Linux) Learn 2 topics at the same time - Python programming & Ethical Hacking Start from 0 up to a high-intermediate level Write over 20 ethical hacking and security programs Learn by example, by writing exciting programs Model problems, design solutions & implement them using Python Write programs in Python 2 and 3 Write cross platform programs that work on Windows, OS X & Linux Have a deep understanding on how computer systems work Have a strong base & use the skills learned to write any program even if its not related to hacking Understand what is Hacking, what is Programming, and why are they related Design a testing lab to practice hacking & programming safely Interact & use Linux terminal Understand what MAC address is & how to change it Write a python program to change MAC address Use Python modules and libraries Understand Object Oriented Programming Write object oriented programs Model & design extendable programs Write a program to discover devices connected to the same network Read, analyse & manipulate network packets Understand & interact with different network layers such as ARP, DNS, HTTP ....etc Write a program to redirect the flow of packets in a network (arp spoofer) Write a packet sniffer to filter interesting data such as usernames and passwords Write a program to redirect DNS requests (DNS Spoofer) Intercept and modify network packets on the fly Write a program to replace downloads requested by any computer on the network Analyse & modify HTTP requests and responses Inject code in HTML pages loaded by any computer on the same network Downgrade HTTPS to HTTP Write a program to detect ARP Spoofing attacks Write payloads to download a file, execute command, download & execute, download execute & report .....etc Use sockets to send data over TCP Send data reliably over TCP Write client-server programs Write a backdoor that works on Windows, OS X and Linux Implement cool features in the backdoor such as file system access, upload and download files and persistence Write a remote keylogger that can register all keystrikes and send them by Email Interact with files using python (read, write & modify) Convert python programs to binary executables that work on Windows, OS X and Linux Convert malware to torjans that work and function like other file types like an image or a PDF Bypass Anti-Virus Programs Understand how websites work, the technologies used and how to test them for weaknesses Send requests towebsites and analyse responses Write a program that can discover hidden paths in a website Write a program that can map a website and discover all links, subdomains, files and directories Extract and submit forms from python Run dictionary attacks and guess login information on login pages Analyse HTML using Python Interact with websites using Python Write a program that can discover vulnerabilities in websites Are there any course requirements or prerequisites? Basic IT knowledge No Linux, programming or hacking knowledge required. Computer with a minimum of 4GB ram/memory Operating System: Windows / OS X / Linux Who this course is for: Anybody interested in learning Python programming Anybody interested in learning ethical hacking / penetration testing Instructor User photo Zaid Sabih Ethical Hacker, Computer Scientist & CEO of zSecurity My name is Zaid Al-Quraishi, I am an ethical hacker, a computer scientist, and the founder and CEO of zSecurity. I just love hacking and breaking the rules, but don’t get me wrong as I said I am an ethical hacker. I have tremendous experience in ethical hacking, I started making video tutorials back in 2009 in an ethical hacking community (iSecuri1ty), I also worked as a pentester for the same company. In 2013 I started teaching my first course live and online, this course received amazing feedback which motivated me to publish it on Udemy. This course became the most popular and the top paid course in Udemy for almost a year, this motivated me to make more courses, now I have a number of ethical hacking courses, each focusing on a specific field, dominating the ethical hacking topic on Udemy. Now I have more than 350,000 students on Udemy and other teaching platforms such as StackSocial, StackSkills and zSecurity. Instructor User photo z Security Leading provider of ethical hacking and cyber security training, zSecurity is a leading provider of ethical hacking and cyber security training, we teach hacking and security to help people become ethical hackers so they can test and secure systems from black-hat hackers. Becoming an ethical hacker is simple but not easy, there are many resources online but lots of them are wrong and outdated, not only that but it is hard to stay up to date even if you already have a background in cyber security. Our goal is to educate people and increase awareness by exposing methods used by real black-hat hackers and show how to secure systems from these hackers. Video course
yunfanLu / Awesome Image PriorPriors in Deep Image Restoration and Enhancement: A Survey
xl-tang3 / UAUDeblur(CVPR' 23) Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior
junshengzhou / DeepPriorAssembly[NeurIPS'2024] Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly
strath-ai / Satellite Cloud Removal DipSatellite cloud removal with Deep Image Prior.
beala / Deep Image Prior TensorflowAn implementation of https://dmitryulyanov.github.io/deep_image_prior for tensorflow.
GaryMataev / DeepREDDeepRED: Deep Image Prior Powered by RED
