NHDRRNet-pytorch

📷 NHDRRNet (TIP'20) implementation using PyTorch framework

Introduction

This repository is the implementation of NHDRRNet [2] using PyTorch framework. The author did not open the code, therefore, we create this repository to implement NHDRRNet using PyTorch framework.

Requirements

• PyTorch 1.4+
• Cuda version 10.1+
• OpenCV
• numpy, tqdm, scipy, etc

Getting Started

Download Dataset

The Kalantari Dataset can be downloaded from https://www.robots.ox.ac.uk/~szwu/storage/hdr/kalantari_dataset.zip [2].

Dataset Model Selection

There are two dataset models provided in dataset folder. Using HDRpatches.py will generate patches in patches folder and will cost ~200GB spaces, but it runs faster. Using HDR.py (default) will open image file only when it needs to do so, thus it will save disk space. Feel free to choose the method you want.

Configs Modifications

• You may modify the arguments in Configs() to satisfy your own environment, for specific arguments descriptions, see utils/configs.py.
• You may modify arguments of NHDRRNet to train a better model, for specific arguments descriptions, see config dictionary in models/NHDRRNet.py.

Train

python train.py

Test

First, make sure that you have models (checkpoint.tar) under checkpoint_dir (which is defined in Configs()).

python test.py

Note. test.py will dump the result images in sample folder.

Tone-mapping (post-processing)

Generated HDR images are in .hdr format, which may not be properly displayed in your image viewer directly. You may use Photomatix for tonemapping [2]:

• Download Photomatix free trial, which won't expire.
• Load the generated .hdr file in Photomatix.
• Adjust the parameter settings. You may refer to pre-defined styles, such as Detailed and Painterly2.
• Save your final image in .tif or .jpg.

Reference

[1] Yan, Qingsen, et al. "Deep hdr imaging via a non-local network." IEEE Transactions on Image Processing 29 (2020): 4308-4322.

[2] elliottwu/DeepHDR repository: https://github.com/elliottwu/DeepHDR