Few-shot Deep HDR Deghosting

This repository contains code and pretrained models for our paper: Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting accepted to IEEE Transactions on Computational Imaging.

It has been tested on RTX 6000 with Tensorflow 2.3.4.

Installation and Setup

Docker Environment:

Getting base image:

$ docker pull tensorflow/tensorflow:2.3.4-gpu

Running base image:

$ docker run --rm -it tensorflow/tensorflow:2.3.4-gpu bash

Installing dependencies:

(docker)# apt update
(docker)# apt install -y ffmpeg libsm6 libxext6 libxrender-dev
(docker)# pip install opencv-python

Datasets:

The Kalantari dataset (SIG17) can be downloaded here, and the Prabhakar dataset (ICCP19) can be downloaded here.

Download the required dataset and extract it in the dataset folder.

Training

To view all training options, run

$ python main.py --help

To train a Bidirectional SGM model with the default configuration, run

$ python main.py --rtx

Inference

To evaluate the pretrained model on the Kalantari17 dataset, run

$ python val.py --rtx --weights pretrained_weights/UCSD/bidirectional.sgm/bidirectional.sgm.tf

Citation

When citing this work, you should use the following Bibtex:

@ARTICLE{9540317, 
    author={Kathirvel, Ram Prabhakar and Agrawal, Susmit and Radhakrishnan, Venkatesh Babu},
    journal={IEEE Transactions on Computational Imaging}, 
    title={Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting}, 
    year={2021},
    volume={},
    number={},
    pages={1-1},
    doi={10.1109/TCI.2021.3112920}
}