lide 1.0.0

Local Intensity Distribution Equalization – Python port of github.com/peune/lide

lide-python — Image Enhancement Library

Python port of peune/lide (C++), which accompanies the paper:

Image enhancement using local intensity distribution equalization https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-015-0085-2

Also includes Pixel Sorting (PS1/PS2) from:

Image enhancement by pixels sorting Sanparith Marukatat and Pichid Kittisuwan https://ieeexplore.ieee.org/document/8619964

Install

pip install lide

Or directly from GitHub:

pip install git+https://github.com/peune/lide-python.git

Methods

ID	Name	Description
0	HE	Standard Histogram Equalization
1	AHE	Adaptive Histogram Equalization (CLAHE-style clipping)
2	DHE	Dynamic Histogram Equalization
3	MHE	Multi-interval Histogram Equalization
4	ESIHE	Exposure-based Sub-Image HE
5	BPHEME	Brightness-Preserving HE using Mean
6	FHSABP	Flattest Histogram Spec. with Adaptive Brightness Preservation
7	HEGMM	HE using Gaussian Mixture Model
8	LIDEG	LIDE simple – Gaussian model
9	LIDEL	LIDE simple – Laplacian model
10	LIDEGMM	LIDE mixture – Gaussian model
11	LIDELMM	LIDE mixture – Laplacian model
12	PS1	Pixel Sorting variant 1
13	PS2	Pixel Sorting variant 2

Python API

import cv2
from lide import color_enhance, EnhanceParam, ENHANCE_LIDEG, ENHANCE_HE

img = cv2.imread("input.jpg")   # BGR uint8

# Standard histogram equalization
param = EnhanceParam(method=ENHANCE_HE)
out = color_enhance(img, param)
cv2.imwrite("out_he.jpg", out)

# LIDE (Gaussian, local window 100px)
param = EnhanceParam(method=ENHANCE_LIDEG, d=100, lide_sigma_min=30.0)
out = color_enhance(img, param)
cv2.imwrite("out_lide.jpg", out)

Grayscale only

import cv2
from lide import enhance, EnhanceParam, ENHANCE_MHE

gray = cv2.imread("input.jpg", cv2.IMREAD_GRAYSCALE)
param = EnhanceParam(method=ENHANCE_MHE, mhe_mmin=5, mhe_mmax=10)
out = enhance(gray, param)
cv2.imwrite("out_mhe.jpg", out)

Command-line

lide -in input.jpg -out output.jpg -method 8
lide -in input.jpg -out output.jpg -method 1 -AHE:clipping 0.03 -ALL:d 50
lide -in input.jpg -out output.jpg -method 13 -PS:wei 5.0

Run lide --help for all options.

Dependencies

• numpy >= 1.24
• opencv-python >= 4.7
• scipy >= 1.10