eyesea_image-preprocessing-algorithms's Introduction

Preprocessing for EyeSea

Preprocessing experiments and applicators for EyeSea.
Numerically optimized matrix computation implementation of image enhancement and color restoration algorithms.
Callable image enhancement and restoration APIs.

Procedure to Execute

Place the input images in data/input/ directory of the corresponding processing operation (i.e Color Restoration or Enhancement).
Edit the corresponding run.py script to uncomment the algorithm to be applied.
Execute the script:
- To only generate the images: python run.py.
- To visualize before-after stages for each image: python run.py --visualize.
The processed images are stored in the corresponding data/output/ directory.

CLAHE: Contrast limited adaptive histogram equalization.
GC: Gamma Correction.
HE: Image enhancement by histogram transformation.
ICM: Underwater Image Enhancement Using an Integrated Colour Model.
Rayleigh Distribution: Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching.
RGHS: Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition.
UCM: Enhancing the low quality images using Unsupervised Colour Correction Method.
UMASK: Unsharp Masking.
BF: Bilateral Filtering.

DCP: Single Image Dehazing using Dark Channel Prior.
GB Dehazing R Correction: Single underwater image restoration by blue-green channels dehazing and red channel correction.
IBLA: Underwater Image Restoration Based on Image Blurriness and Light Absorption.
LowComplexityDCP: Low Complexity Underwater Image Enhancement Based on Dark Channel Prior.
MIP: Initial results in underwater single image dehazing.
New Optical Model: Single underwater image enhancement with a new optical model (paper).
RoWS: Removal of water scattering.
UDCP: Transmission Estimation in Underwater Single Images.
ULAP: A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration.

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