(CVPR 2018) Zero-Shot Super-Resolution using Deep Internal Learning

Shocher A, Cohen N, Irani M. “Zero-Shot” Super-Resolution using Deep Internal Learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3118-3126.

1. Overview

1.1. Motivation

  • Existing SR supervised method rely on prior training (restricted to training data)
  • Non-ideal acquisition process in reality (not always bicubic, bilinear), old photo, noisy image, biological data, phone image

  • Natural images have strong internal data repetition (at different location and scale). Internal entropy of patches inside a single image is much smaller than the external entropy of patches in a general collection of natural images

论文提出Zero-Shot SR方法

  • Not rely on prior training
  • Exploit internal recurrence of information inside a single image
  • Perform SR on real image where the acquisition process is unknown and non-ideal
  • Train a small CNN at test time only from the LR image and its downscaled (self-supervision)
  • train + test time is comparable to the test time of SotA

1.2. Contribution

  • First unsupervised CNN-based SR method
  • Handle non-ideal condition
  • Not require pretraining
  • SR any size


  • EDSR
  • VDSR

Unsupervised (not deep learning)

  • Blind-Deblurring
  • Blind-Dehazing
  • Blind-SR

1.4. Model

  • Downscaling test image to many smaller version of itself (HR-fater vs LR-son).
  • Gradually SR. Several intermediate scale-factors
  • 4 Rotations (0, 90, 180, 270) and 2 flip
  • Learn residual
  • Select rate based on proportional to the size of the HR-father. Size close to test image has high rate
  • 8 output of test image (4R x 2F), take median + back-projection
  • More intermediate scale-factor increase accuracy
  • Time independent of image size and scale-factor
  • 54 s/img on GPU, EDSR+ 20s on 200x200 image, EDSR 5min on 800x800 image

1.5. Parameters

  • Downscaling kernel
  • Scale-factor
  • Number of gradual scale increase
  • Whether Backprojection
  • Whether add noise (learn to ignore uncorrelated cross-scale information)

1.6. Future Work

  • Combine Internal-Learning with External-Learning in a single computational framework

2. Experiments

2.1. Ideal Case (bicubic kernel)

2.2. Non-ideal Case

2.2.1. Deviate from bicubic

  • Random Gaussian Kernel

  • Two case apply to ZSSR

    • Blind-SR evaluate kernel, feed to ZSSR
    • True kenel feed to ZSSR
  • Solution

    • Accurate downscaling model is more important than sophisticated image prior
    • Wrong downscaling kernel lead to oversmoothed SR result

2.2.2. Low-quality LR image

  • Gaussian noise
  • Speckle noise
  • JPEG compression