1. Learning to See in the Dark
Published at the Conference on Computer Vision and Pattern Recognition (CVPR 2018)
Presented by:
Nafiseh Vahabi

2. Challenges of fast imaging in low light
Low photon count
Low SNR
Short-exposure images suffer from noise
Long exposure can induce blur and is often impractical.
This challenge is addressed by:
Introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images
Develop a pipeline for processing low-light images, based on end-to-end training of a fully- convolutional network.

3. Camera Setting The network operates directly on raw sensor data
Camera setting : (Two cameras)
Illumination < 0.1 lux at the camera
Exposure time : 1/30
Aperture : f/5.6

4. SID (See in the Dark) Dataset
Dataset contains 5094 raw short-exposure images, each with the reference long-exposure image.

5. Example of Dataset 0.2 < outdoor illumination < 5

6. Existing Methods

7. ConvNet Structure Two structure:
Multi-scale context aggregation network
U-net (default )
Memory consumption (full resolution image in GPU)
The amplification ratio determined the brightness of output (set externally)

8. Result of Two Structures

9. Training Training from scratch using L1 and Adam optimizer
Input: raw data, short exposure image
Ground Truth: Corresponding long exposure image in RGB
Network trained for each camera
Amplification ratio is set to be a difference between the input and reference images
Learning rate:
Training proceeds to 4000 epochs

10. Evaluate the Proposed Methods

11. Evaluate the Proposed Methods

12. Conclusion Successful noise suppression Correct color transformation
Limitation
No humans or dynamic objects
The result is imperfect and need to improve
Application ratio set externally
Improve runtime optimisation