GitHub | 您所在的位置:网站首页 › picture processing › GitHub |
By Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao. [arXiv] We study the low-level computer vision task (such as denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). We present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks.
The benchmark datasets can be downloaded as follows: For super-resolution: Set5, Set14, B100, Urban100. Please refer to EDSR-PyTorch For denoising: CBSD68, Urban100. For deraining: Rain100. Please refer to PReNet The result images are converted into YCbCr color space. The PSNR is evaluated on the Y channel only. Script DescriptionThis is the inference script of IPT, you can following steps to finish the test of image processing tasks, like SR, denoise and derain, via the corresponding pretrained models. Script ParameterFor details about hyperparameters, see option.py. Evaluation Pretrained modelsThe pretrained models are available in google drive Evaluation ProcessInference example: For SR x2,x3,x4: python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --data_test Set5+Set14+B100+Urban100 --scale $SCALE --test_onlyNote: path of images should be like: $MODEL_PATH/benchmark/Set5/HR/XXX.png and $MODEL_PATH/benchmark/Set5/LR_bicubic/XXX.png For Denoise 30,50: python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --data_test CBSD68+Urban100 --scale 1 --denoise --sigma $NOISY_LEVEL --test_onlyNote: path of images should be like: $MODEL_PATH/benchmark/CBSD68/XXX.png For derain: python main.py --dir_data $DATA_PATH --pretrain $MODEL_PATH --scale 1 --derain --test_onlyNote: path of images should be like: $MODEL_PATH/Rain100L/rain-XXX.png and $MODEL_PATH/Rain100L/norain-XXX.png Results Detailed results on image super-resolution task. Method Scale Set5 Set14 B100 Urban100 VDSR X2 37.53 33.05 31.90 30.77 EDSR X2 38.11 33.92 32.32 32.93 RCAN X2 38.27 34.12 32.41 33.34 RDN X2 38.24 34.01 32.34 32.89 OISR-RK3 X2 38.21 33.94 32.36 33.03 RNAN X2 38.17 33.87 32.32 32.73 SAN X2 38.31 34.07 32.42 33.1 HAN X2 38.27 34.16 32.41 33.35 IGNN X2 38.24 34.07 32.41 33.23 IPT (ours) X2 38.37 34.43 32.48 33.76 Method Scale Set5 Set14 B100 Urban100 VDSR X3 33.67 29.78 28.83 27.14 EDSR X3 34.65 30.52 29.25 28.80 RCAN X3 34.74 30.65 29.32 29.09 RDN X3 34.71 30.57 29.26 28.80 OISR-RK3 X3 34.72 30.57 29.29 28.95 RNAN X3 34.66 30.52 29.26 28.75 SAN X3 34.75 30.59 29.33 28.93 HAN X3 34.75 30.67 29.32 29.10 IGNN X3 34.72 30.66 29.31 29.03 IPT (ours) X3 34.81 30.85 29.38 29.49 Method Scale Set5 Set14 B100 Urban100 VDSR X4 31.35 28.02 27.29 25.18 EDSR X4 32.46 28.80 27.71 26.64 RCAN X4 32.63 28.87 27.77 26.82 SAN X4 32.64 28.92 27.78 26.79 RDN X4 32.47 28.81 27.72 26.61 OISR-RK3 X4 32.53 28.86 27.75 26.79 RNAN X4 32.49 28.83 27.72 26.61 HAN X4 32.64 28.90 27.80 26.85 IGNN X4 32.57 28.85 27.77 26.84 IPT (ours) X4 32.64 29.01 27.82 27.26 Super-resolution result
|
CopyRight 2018-2019 实验室设备网 版权所有 |