Context 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

Developed VisionSoC, an advanced image upscaling model using Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) with Python, leveraging frameworks such as TensorFlow and Keras. Created a comprehensive web-based application for the model using HTML, CSS, and JavaScript, and integrated the frontend with the backend using Flask.

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图像处理接口：图像解模糊（deblurring）和图像超分辨率还原（Super-resolution）深度学习框架tensorflow和torch，并实现web后端基于python-Flask框架的接口，python语言

webGL implementation of AMDs fidelityFX Super Resolution

Web based voice talk application using Audio Super Resolution and RNNoise(called ASRNNoise). PNU-CSE-2018-graduate assignment

Web-based video super-resolution application using SRGAN implementation in TensorFlow

A DeepLearning web project using ESRGAN and Django

Web app that allows users to upload images, enhance their resolution, and download the improved images.

A browser-based super-resolution inference tool that enhances image quality using AI models. This project allows users to upscale images directly in the web browser without needing to rely on server-side processing.

Pixelfix is a web service that enhances the image quality by providing automatic image super resolution and image denoising based on two accurate and lightweight deep learning models.

Serving the GAN based image super resolution model in a FLASK web app

a super resolution achievement on web using tensorflow.js

Browser-first AI toolbox: super-resolution, TTS, and offline ASR powered by ONNXRuntime-Web/WebGPU

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simple, fast audio-super-resolution tensorflow.js model

使用Real-CUGAN模型的web application

a web app which can make super resolution images,inpaint images and de-noise images provided to them using deep learning

web server for super-resolution CNN

Web Application for super resolution of Depth elevation map (DEM) using python framework flask for backend.

A web app that enables users to upscale images using a machine learning model trained through adversarial learning

Web App for Neural Style Transfer and Super resolution

a unsupervised experiment of Text Super Resolution for web page based on different ttf

A production-ready super-resolution system for enhancing low-resolution images using state-of-the-art deep learning models. This project provides multiple interfaces (CLI, Web UI, Python API) and supports various super-resolution architectures.

A Dockerized full-stack web application for Real-World Image Super Resolution and Face Enhancement. Built with React, Nginx, and Python (FastAPI).

This project uses a Super-Resolution Generative Adversarial Network (SRGAN) to enhance the resolution of images by 4x. Users can upload low-resolution images, and the web application—built using Flask—processes these images with a pre-trained SRGAN model, returning high-quality images.

AI-powered image enhancement web service with super-resolution, low-light enhancement, color correction, and face restoration. Deploy to HuggingFace Spaces for free GPU hosting.

enhance.ai is a web app. that uses state-of-the-art Deep Learning Techniques for Single Image Super Resolution (SISR) :low_brightness: :arrow_forward: :high_brightness:

AI-powered image restoration app using ESRGAN deep learning model. Upload old, damaged, or low-resolution photos and restore them with cutting-edge super-resolution technology. Built with Streamlit, TensorFlow, and TensorFlow Hub. Simple web interface for enhancing and upscaling images instantly.