Hello. According to your paper, global alignment is performed using a similarity transformation in the preprocessing stage. I wonder if this global alignment process is performed during training phase in the code because I couldn't find it. Thank you!

Thanks to the author for the code, it's a very good work. In the process of reproduction, I want to test the visualization effect of coarsenet, but I don't know how to do it？

您好，您的这篇工作十分精彩，算法性能也很优异，您在论文中提到的对比算法中有一个算法是19年提出的多曝光图像融合算法AHDRNet，想请问一下您是怎么用HDRVideo这篇工作的数据集去训练AHDRNet的呢（注意到您这篇文章训练的数据集代码中对参考帧有一个tone perturbation的操作，在AHDRNet训练过程中是否也需要有这个操作？），我按照您这篇工作中提供的数据集处理代码训练了一下AHDRNet，但是效果并不理想，在合成的测试集（两曝光为例）上远没有达到Psnr=39.05，尤其是参考帧为高曝光帧的时候，并且用其算法恢复出的视频效果并不理想，您是否方便提供一下您复现AHDRNet的训练代码，十分感谢！！

@guanyingc

I try to reimplement the performance (PSNR) of the real-world dataset with your released code and model. However, I found that the reimplement performance is lower than the performance released in the paper, no matter is static scenes or dynamic scenes.

I am sure that I do not modify any code. If it is normal to get such results?

Thanks a lot and hope for your quick reply.

Hi, Thanks to the author for the code, it really help me a lot. In the process of retraining the model, we set make_dir as True.We only modify dataset_dir in train_opt.py. In the code, an error occurred in the creation the saving folder. The error is as follows:

Traceback (most recent call last): File "main.py", line 8, in log = logger.Logger(args) File "I:\DeepHDRVideo-master\utils\logger.py", line 29, in init self._setup_dirs(args) File "I:\DeepHDRVideo-master\utils\logger.py", line 76, in _setup_dirs self.log_fie = open(file_dir, 'w') FileNotFoundError: [Errno 2] No such file or directory: 'logdir/syn_vimeo_dataset\ICCV\11-28,spynet_2triple,weight_net,LReLU,hdr3E_flow_model,kaiming,l1,cm_d-256,cr_h-256,ht_r-0.9,ba_h-16,in_r-0.0001,in_ldr,sc_h-320,concat\11-28,spynet_2triple,weight_net,LReLU,hdr3E_flow_model,kaiming,l1,cm_d-256,cr_h-256,ht_r-0.9,ba_h-16,in_r-0.0001,in_ldr,sc_h-320,concat,14:47:07'

Whether my settings are wrong. Hope to get your help, thank you!

Dear author,

I have some questions and hope to get some suggestions from you.

1. Have you ever tested models without vimeo-90k training data? Is your method still better than Kalantari's in such condition? I just want to know how effective training with vimeo is.
2. I found that the ground truth of vimeo data is generated by converting the LDR images to linear HDR images for each single one. I'm a little worried about whether this data can work and whether it is meaningful. (because the ground truth of some real-world datasets is generated by merging multi-exposed still images like [1] and your paper )

Looking forward to your reply!

Reference: [1] Kalantari, Nima Khademi, and Ravi Ramamoorthi. "Deep high dynamic range imaging of dynamic scenes." ACM Trans. Graph. 36.4 (2017): 144-1.

Dear guanying， The file (TOG13 dataset) you provided in google drive already contains the hdr images , I want to know if those hdr images are predictd by the network you proposed in paper ？

Thanks for releasing the code and dataset! I have some questions about HDR-VDP-2: First, I found that you adopted 'sRGB-display' as the mode of HDR-VDP-2. I think this mode is for inputting images in the pixel domain. In this mode, HDR-VDP-2 will map input images from the pixel domain to the linear domain. However, the input images in your code are normalized HDR (linear) images. Second, other modes in HDR-VDP-2 require absolute luminance values rather than normalized values. I wonder how to compute this metric. Thank you!

Dear Guanying,

I have a question about the datasets, as noted in paper，you used Vimeo-90K to train the network，Vimeo-90K is a very big dataset，and I want to konw if you used all 91, 701 preprocessed 7-frame clips to train the network，or you just used a part of Vimeo-90K to train the network？ In addition，What's the performance of the algorithm if we don't use Vimeo to train the network？ I wonder if the performance will degrades a lot？

Looking forward to your reply！

Dear Guanying,

I have a question about the comparative experiment, and I hope you could give some suggestions.

You give results with your method and Kalantari19's method to compare. However, in Kalantari19's paper they said " use mini-batches of size 10 and perform training for 60,000 iterations" without specifying concrete number of epochs. So I wonder how you train their method and how epochs you use for training Kalantari19's with your dataset.

Looking forward to your reply. Thanks.

Hi, many thanks for your work and codes. I tested the pretrained model on GPU and it works. But I tried to retrained the model but I got cudnn error like" File "main.py", line 33, in main(args) File "main.py", line 21, in main train_utils.train(args, log, train_loader, model, epoch, recorder) File "/media/ye/ssd11/DeepHDRVideo-master/utils/train_utils.py", line 17, in train pred = model.forward(split='train'); File "/media/ye/ssd11/DeepHDRVideo-master/models/hdr2E_flow_model.py", line 93, in forward self.fpred = self.fnet(fnet_in) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward return self.module(*inputs[0], **kwargs[0]) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/media/ye/ssd11/DeepHDRVideo-master/models/archs/flow_networks.py", line 94, in forward flow1, flow2 = self.moduleBasici File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/media/ye/ssd11/DeepHDRVideo-master/models/archs/flow_networks.py", line 33, in forward flows = self.moduleBasic(inputs).clamp(-320, 320) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, **kwargs) File "/home/ye/.opt/miniconda/envs/torch11/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 338, in forward self.padding, self.dilation, self.groups) RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED " And I checked the codes and there is no setting for cudnn. So could you help me for this problem? Many thansk.

Dear author

I forget to set the model to record the mnet2_15.pth, it just record the 14th epoch. So I change the start_epoch to 14 and add the path of 14th epoch's checkpoint. But it seems doesn't work. So what should I do to load the checkpoint in training stage 2?

Hi,

I notice in one issue, you think vimeo-90k dataset is not perfect but effective in getting the higher performance. So now I'm preparing training with/without it to see the difference and considering how to make another suitable synthetic dataset.

However, after training without vimeo-90k, I found results on test sequence Carousel_fireworks are obviously lower than other sequences. PSNR on Carousel_fireworks is 31.62, while PSNR on Poker_fullshot is 41.31 (2-alternating exposed inputs). Although training with vimeo-90k is not finished, I think maybe PSNR on Carousel_fireworks is still bad.

So do you also get a bad PSNR score on Carousel_fireworks in your work? And I also wonder about the reason.

Hi, i'm testing your code on my own datasets, captured with a Canon and preprocessed as required. In particular i'm using sequences of three alternating exposures. When i have a configuration {EV-2, EV+0, EV+2, ...} your code works fine. Instead, when i have a configuration {EV-3, EV+0, EV+3, ...}, i get 'periodically' flickering: i.e., if H_i is a HDR frame, it doesn't match with H_{i+1} and H_{i+2} (flickering), but it matches with H_{i+3}. Why do i get this flickering problem? I attach two hdr video of a static scene to show this. Below I specify exposure times (in seconds). Aperture and iso are constant in both (ISO 800, f3.5). 1- First HDR video: {EV-2, EV+0, EV+2, ...}, exposure times {1/320, 1/80, 1/20} 2- Second HDR video: {EV-3, EV+0, EV+3, ...}, exposure times {1/640, 1/80, 1/10}

https://user-images.githubusercontent.com/114491838/194347916-ccb60538-4562-4dd9-84ec-333cf1938cd3.mov

https://user-images.githubusercontent.com/114491838/194347942-72cdfb11-d243-4c94-8f43-5bbce5860631.mov