Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)
[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment
Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment
Python library for realistically degrading images.
[ICCV 2025] - Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution
🔷 Effects of Degradations on Deep Neural Network Architectures (official code).
An AI restoration suite for cel animation, featuring specialized Real-ESRGAN models and the physics-based degradation simulator used to train them.
Can we perform face hallucination using limited set of unaligned pairs?
Using an already existing ESRGAN model, degrading our HR images to generate training data for finetuning and further checking the MSE and PSNR for the generated and the original HR images. Link to the original link of Real ESRGAN Repo - https://github.com/xinntao/Real-ESRGAN
A robust learning scheme by proposing a novel augmentation algorithm that scales to emulate a large set of image degradation and occlusion policies
Realistic synthetic degradation of document images — simulate scans, phone photos, photocopies, and aged paper for data augmentation in document AI / OCR / layout analysis pipelines. 23 physically-motivated transforms, 6 presets, CLI + Python API.
Includes image smoothing, sharpening, and hough transfer.
Digital Image Processing Course | Home Works Design| Fall 2021 | Dr. MohammadReza Mohammadi
🎨 Enhance cel animation with specialized AI models and a physics-based simulator for effective restoration of historical animation defects.
Deep-learning project for robust AI-generated face detection, comparing degradation-aware training, expert routing and unified detection with PyTorch and ResNet-18.
FaceTwin Project public dataset (CC BY 4.0) — three production prompts, six image-degradation profiles, sample-output metadata. Companion data for https://pleasejuststop.org and https://prankmyface.lol.
Robust CNN & CapsuleNet benchmarks for noisy images – modern TensorFlow 2/Keras 3 support.
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