Invertible Image Rescaling

This is the PyTorch implementation of paper: Invertible Image Rescaling (ECCV 2020 Oral). arxiv.

Dependencies and Installation

Python 3 (Recommend to use Anaconda)
PyTorch >= 1.0
NVIDIA GPU + CUDA
Python packages: pip install numpy opencv-python lmdb pyyaml
TensorBoard:
- PyTorch >= 1.1: pip install tb-nightly future
- PyTorch == 1.0: pip install tensorboardX

Dataset Preparation

Commonly used training and testing datasets can be downloaded here.

Get Started

Training and testing codes are in 'codes/'. Please see 'codes/README.md' for basic usages.

Invertible Architecture

Quantitative Results

Quantitative evaluation results (PSNR / SSIM) of different downscaling and upscaling methods for image reconstruction on benchmark datasets: Set5, Set14, BSD100, Urban100, and DIV2K validation set. For our method, differences on average PSNR / SSIM from different z samples are less than 0.02. We report the mean result over 5 draws.

Downscaling & Upscaling	Scale	Param	Set5	Set14	BSD100	Urban100	DIV2K
Bicubic & Bicubic	2x	/	33.66 / 0.9299	30.24 / 0.8688	29.56 / 0.8431	26.88 / 0.8403	31.01 / 0.9393
Bicubic & SRCNN	2x	57.3K	36.66 / 0.9542	32.45 / 0.9067	31.36 / 0.8879	29.50 / 0.8946	–
Bicubic & EDSR	2x	40.7M	38.20 / 0.9606	34.02 / 0.9204	32.37 / 0.9018	33.10 / 0.9363	35.12 / 0.9699
Bicubic & RDN	2x	22.1M	38.24 / 0.9614	34.01 / 0.9212	32.34 / 0.9017	32.89 / 0.9353	–
Bicubic & RCAN	2x	15.4M	38.27 / 0.9614	34.12 / 0.9216	32.41 / 0.9027	33.34 / 0.9384	–
Bicubic & SAN	2x	15.7M	38.31 / 0.9620	34.07 / 0.9213	32.42 / 0.9028	33.10 / 0.9370	–
TAD & TAU	2x	–	38.46 / –	35.52 / –	36.68 / –	35.03 / –	39.01 / –
CNN-CR & CNN-SR	2x	–	38.88 / –	35.40 / –	33.92 / –	33.68 / –	–
CAR & EDSR	2x	51.1M	38.94 / 0.9658	35.61 / 0.9404	33.83 / 0.9262	35.24 / 0.9572	38.26 / 0.9599
IRN (ours)	2x	1.66M	43.99 / 0.9871	40.79 / 0.9778	41.32 / 0.9876	39.92 / 0.9865	44.32 / 0.9908

Downscaling & Upscaling	Scale	Param	Set5	Set14	BSD100	Urban100	DIV2K
Bicubic & Bicubic	4x	/	28.42 / 0.8104	26.00 / 0.7027	25.96 / 0.6675	23.14 / 0.6577	26.66 / 0.8521
Bicubic & SRCNN	4x	57.3K	30.48 / 0.8628	27.50 / 0.7513	26.90 / 0.7101	24.52 / 0.7221	–
Bicubic & EDSR	4x	43.1M	32.62 / 0.8984	28.94 / 0.7901	27.79 / 0.7437	26.86 / 0.8080	29.38 / 0.9032
Bicubic & RDN	4x	22.3M	32.47 / 0.8990	28.81 / 0.7871	27.72 / 0.7419	26.61 / 0.8028	–
Bicubic & RCAN	4x	15.6M	32.63 / 0.9002	28.87 / 0.7889	27.77 / 0.7436	26.82 / 0.8087	30.77 / 0.8460
Bicubic & ESRGAN	4x	16.3M	32.74 / 0.9012	29.00 / 0.7915	27.84 / 0.7455	27.03 / 0.8152	30.92 / 0.8486
Bicubic & SAN	4x	15.7M	32.64 / 0.9003	28.92 / 0.7888	27.78 / 0.7436	26.79 / 0.8068	–
TAD & TAU	4x	–	31.81 / –	28.63 / –	28.51 / –	26.63 / –	31.16 / –
CAR & EDSR	4x	52.8M	33.88 / 0.9174	30.31 / 0.8382	29.15 / 0.8001	29.28 / 0.8711	32.82 / 0.8837
IRN (ours)	4x	4.35M	36.19 / 0.9451	32.67 / 0.9015	31.64 / 0.8826	31.41 / 0.9157	35.07 / 0.9318

peterzhousz / invertible-image-rescaling Goto Github PK