HR_to_LR

scripts to process HR image to LR, you can customize the process.

Dependencies

• opencv-python
• json

Usage

1. First modify the json file in config/

Here is an example of the process.json file.

{
	"in_path":"your location"
	,"out_path":"your location"
	,"noise_level":4
	,"poisson_level":30
	,"sinc_prob": 0.5
	,"multi-thread": false
	,"is_random": false
	,"process":
	{
	"process1":{
		"data_num_ratio": 1
		, "pipline":["downsample", "blur", "noise", "upsample", "sinc", "jpeg", "noise"]
		, "downsample_method":"bicubic"
		, "downsample_scale": 0.5
		, "blur_sigma_up": 0.8
		, "blur_sigma_down": 0.5
		, "jpeg_quality_l": 30
		, "jpeg_quality_h": 95
		, "upsample_method":"bicubic"
		, "upsample_scale": 2
		}
	}
}

The pipline can include the following properties:

• downsample
• upsample
• noise(gauss noise sigma random from [1, noise_level], and poisson noise from [5, possion_level] / 100)
• jpeg(random from [jpeg_quality_l, jpeg_quality_h]),now using opencv encode implementation
• blur(GaussianBlur from [blur_sigma_down, blur_sigma_up])
• sinc_filter(can introduce ringing and overshoot artifacts)
• rot_blur(rotate image and then rotate back, can cause blur and edge artifacts)
• patch_noise(can add noise from a patch)
• fixed_downsample(sample method which fixes the size, the stable version of downsample/upsample)

to debug or steadily generate pictures, we suggest you set "multi-thread": false

2. Then run

python demo.py -opt config/process.json

TODO

• add sinc_filter
• add rot_blur
• add fixed_sample
• add Multithreading Implementation
• add different type of noise(now possion, gaussian, noise_multi)
• add random mode
• add random dropout prob mode