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simple binary neural network framwork of single image super-resolution

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introduction

a simple binary neural network for SISR(base on EDSR official code)

if you want to try different binary function, you need to :

  1. add new script to model/binarize and modify model/init.py to choose your binary function.
  2. edit run.sh(view this script for more detail), choose one function you want to run.
  3. run sh run.sh

performance

an example binary function: bianrize/WApproxASTE.py:

weight binarization

forward:

backward,use approxsign (bireal-net's method)

activation binarization

forward:

backward,STE

network modification

add an Tanh() function before binact. It helps to transfer a nonlinear gradient after STE.

srresnet

Method Bit Set5 Set14 B100 Urban100
W/A PSNR/SSIM PSNR/SSIM PSNR/SSIM PSNR/SSIM
srresnetx2 32/32 37.889/0.958 33.4/0.915 32.077/0.896 31.602/0.922
Oursx2 1/1 36.345/0.934 32.221/0.876 31.364/0.877 29.407/0.883
Oursx2(DBSR) 1/1 37.502 33.085 31.82 30.881
FPx4 32/32 32.066/0.890 28.497/0.778 27.516/0.731 25.858/0.778
Oursx4 1/1 31.232/0.848 28.047/0.729 27.215/0.699 25.075/ 0.727
Oursx4(DBSR) 1/1 31.666 28.282 27.333 25.419

compare to Efficient Super Resolution Using Binarized Neural Network(Ma et.al.)

Method Bit Set5 Set14 Urban100
W/A PSNR PSNR PSNR
Ma et.al. x2 1/1 35.66 31.56 28.76
Oursx2 1/1 36.345 32.221 29.407
Ma et.al. x4 1/1 30.34 27.16 24.48
Oursx4 1/1 31.232 28.047 25.075

edsr

Method Bit Set5 Set14 B100 Urban100
W/A PSNR/SSIM PSNR/SSIM PSNR/SSIM PSNR/SSIM
edsrx2 32/32 37.931/0.958 33.459/0.915 32.102/0.896 31.709/0.923
Oursx2 1/1 37.425/0.941 32.942/0.887 31.717/0.883 30.425/0.899

use pretrain

Method Bit Set5 Set14 B100 Urban100
W/A PSNR/SSIM PSNR/SSIM PSNR/SSIM PSNR/SSIM
edsrx2 32/32 37.931/0.958 33.459/0.915 32.102/0.896 31.709/0.923
Oursx2 1/1 37.425/0.941 32.971/0.887 31.735/0.883 30.507/ 0.900

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simple binary neural network framwork of single image super-resolution

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