Fast, Accurate and Lightweight Super-Resolution models

We present FALSR A,B,C models. The metrics and results can be generated with,

$ python3 calculate.py --pb_path ./pretrained_model/FALSR-A.pb --save_path ./result/

Comparison with state-of-the-art methods

Method	MulAdds	Params	Set5	Set14	BSD100	Urban100
SRCNN	52.7G	57K	36.66/0.9542	32.42/0.9063	31.36/0.8879	29.50/0.8946
FSRCNN	6.0G	12K	37.00/0.9558	32.63/0.9088	31.53/0.8920	29.88/0.9020
VDSR	612.6G	665K	37.53/0.9587	33.03/0.9124	31.90/0.8960	30.76/0.9140
DRCN	17,974.3G	1,774K	37.63/0.9588	33.04/0.9118	31.85/0.8942	30.75/0.9133
CNF	311.0G	337K	37.66/0.9590	33.38/0.9136	31.91/0.8962	-
LapSRN	29.9G	813K	37.52/0.9590	33.08/0.9130	31.80/0.8950	30.41/0.9100
DRRN	6,796.9G	297K	37.74/0.9591	33.23/0.9136	32.05/0.8973	31.23/0.9188
BTSRN	207.7G	410K	37.75/-	33.20/-	32.05/-	31.63/-
MemNet	2,662.4G	677K	37.78/0.9597	33.28/0.9142	32.08/0.8978	31.31/0.9195
SelNet	225.7G	974K	37.89/0.9598	33.61/0.9160	32.08/0.8984	-
CARN	222.8G	1,592K	37.76/0.9590	33.52/0.9166	32.09/0.8978	31.92/0.9256
CARN-M	91.2G	412K	37.53/0.9583	33.26/0.9141	31.92/0.8960	31.23/0.9194
MoreMNAS-A	238.6G	1039K	37.63/0.9584	33.23/0.9138	31.95/0.8961	31.24/0.9187
MoreMNAS-B	256.9G	1118K	37.58/0.9584	33.22/0.9135	31.91/0.8959	31.14/0.9175
MoreMNAS-C	5.5G	25K	37.06/0.9561	32.75/0.9094	31.50/0.8904	29.92/0.9023
MoreMNAS-D	152.4G	664K	37.57/0.9584	33.25/0.9142	31.94/0.8966	31.25/0.9191
FALSR-A (ours)	234.7G	1,021K	37.82/0.9595	33.55/0.9168	32.12/0.8987	31.93/0.9256
FALSR-B (ours)	74.7G	326k	37.61/0.9585	33.29/0.9143	31.97/0.8967	31.28/0.9191
FALSR-C (ours)	93.7G	408k	37.66/0.9586	33.26/0.9140	31.96/0.8965	31.24/0.9187

Citation

Your citations are welcomed!

@inproceedings{chu2020fast,
  title={Fast, accurate and lightweight super-resolution with neural architecture search},
  author={Chu, Xiangxiang and Zhang, Bo and Ma, Hailong and Xu, Ruijun and Li, Qingyuan},
  booktitle={International Conference on Pattern Recognition, ICPR},
  year={2020}
}