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Kernel Modeling Super-Resolution on Real Low-Resolution Images
Ruofan Zhou Sabine Süsstrunk
Image and Visual Representation Lab
École Polytechnique Fédérale de Lausanne
- Supplementary Material -
project page

1. X4 SR results 2. Psychovisual experiment 3. SR examples on real images 4. Experiments on zoom in SR 5. Visualization of kernels

In Section 4.6 in the paper, we present our experiment with different zoom. Here we provide more details about the experiment.
We use a Nikon AF-S 24-70mm zoom lens to collect 3 pairs of images. The RGB image taken with a 70mm focal length serves as the 2X zoom ground truth of the raw sensor data taken with a 35 mm focal length. We set ISO equals to 400. We capture images with a distance of at least 100 meters to avoid perspective shifts.
A slight misalignment is unavoidable because of focal length variations in the center of projection when the lens zooms in and out. We align the bicubic upscaled 35mm "low-resolution" image with the "zoomed-in" groundtruth 70mm image by applying a grid search in horizontal and vertical shifts (within 100 pixels) as well as a stretching (range between 0.9 to 1.1).
Here we show three examples of super-resolution results:

SRCNN[3] | VDSR[4] | EDSR[1] | DBPN[2] | KMSR | refernece

SRCNN[3] | VDSR[4] | EDSR[1] | DBPN[2] | KMSR | refernece

SRCNN[3] | VDSR[4] | EDSR[1] | DBPN[2] | KMSR | refernece

Reference:
[1] Lim Bee, Son Sanghyun, Kim Heewon, Nah Seungjun. Lee Kyoung Mu. "Enhanced Deep Residual Networks for Single Image Super-Resolution", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017
[2] Haris Muhammad, Shakhnarovich Greg, Ukita Norimichi. "Deep Back-Projection Networks for Super-Resolution", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
[3] Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. "Image Super-Resolution Using Deep Convolutional Networks", in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015
[4] Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee. "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016