图像增强之python实现——MSR,MSRCR,MSRCP,autoMSRCR

最近在做街景图象色彩校正方面的工作，对于过暗，过曝光，以及背光等现象，用过一些gamma校正以及其他的方法，最终选择基于Retinex原理的方法对这几种现象都有一定的增强效果。最开始的时候是基于Retinex的单尺度的SSR。原理很多博客都解释了，但是感觉看起来很复杂，我感觉就三个步骤：
1）将图像进行log变换
2）然后将log图像进行高斯模糊
3）利用原图和模糊之后的log图像做差分
之后很多学者有基于这个retinex算法改进了很多其他的算法比如MSR,MSRCR,MSRCP等，我i先放一下校正结果吧，总感觉给代码不给结果就是耍流氓。

图像增强结果：
原图过曝光图像

autoMSRCR

MSRCR

在这里插入图片描述
MSRCP

retinex.py

import numpy as np
import cv2

def singleScaleRetinex(img, sigma):

    retinex = np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sigma))

    return retinex

def multiScaleRetinex(img, sigma_list):

    retinex = np.zeros_like(img)
    for sigma in sigma_list:
        retinex += singleScaleRetinex(img, sigma)

    retinex = retinex / len(sigma_list)

    return retinex

def colorRestoration(img, alpha, beta):

    img_sum = np.sum(img, axis=2, keepdims=True)

    color_restoration = beta * (np.log10(alpha * img) - np.log10(img_sum))

    return color_restoration

def simplestColorBalance(img, low_clip, high_clip):    

    total = img.shape[0] * img.shape[1]
    for i in range(img.shape[2]):
        unique, counts = np.unique(img[:, :, i], return_counts=True)
        current = 0
        for u, c in zip(unique, counts):            
            if float(current) / total < low_clip:
                low_val = u
            if float(current) / total < high_clip:
                high_val = u
            current += c
                
        img[:, :, i] = np.maximum(np.minimum(img[:, :, i], high_val), low_val)

    return img    

def MSRCR(img, sigma_list, G, b, alpha, beta, low_clip, high_clip):

    img = np.float64(img) + 1.0

    img_retinex = multiScaleRetinex(img, sigma_list)

    img_color = colorRestoration(img, alpha, beta)    
    img_msrcr = G * (img_retinex * img_color + b)

    for i in range(img_msrcr.shape[2]):
        img_msrcr[:, :, i] = (img_msrcr[:, :, i] - np.min(img_msrcr[:, :, i])) / \
                             (np.max(img_msrcr[:, :, i]) - np.min(img_msrcr[:, :, i])) * \
                             255
    
    img_msrcr = np.uint8(np.minimum(np.maximum(img_msrcr, 0), 255))
    img_msrcr = simplestColorBalance(img_msrcr, low_clip, high_clip)       

    return img_msrcr

def automatedMSRCR(img, sigma_list):

    img = np.float64(img) + 1.0

    img_retinex = multiScaleRetinex(img, sigma_list)

    for i in range(img_retinex.shape[2]):
        unique, count = np.unique(np.int32(img_retinex[:, :, i] * 100), return_counts=True)
        for u, c in zip(unique, count):
            if u == 0:
                zero_count = c
                break
            
        low_val = unique[0] / 100.0
        high_val = unique[-1] / 100.0
        for u, c in zip(unique, count):
            if u < 0 and c < zero_count * 0.1:
                low_val = u / 100.0
            if u > 0 and c < zero_count * 0.1:
                high_val = u / 100.0
                break
            
        img_retinex[:, :, i] = np.maximum(np.minimum(img_retinex[:, :, i], high_val), low_val)
        
        img_retinex[:, :, i] = (img_retinex[:, :, i] - np.min(img_retinex[:, :, i])) / \
                               (np.max(img_retinex[:, :, i]) - np.min(img_retinex[:, :, i])) \
                               * 255

    img_retinex = np.uint8(img_retinex)
        
    return img_retinex

def MSRCP(img, sigma_list, low_clip, high_clip):

    img = np.float64(img) + 1.0

    intensity = np.sum(img, axis=2) / img.shape[2]    

    retinex = multiScaleRetinex(intensity, sigma_list)

    intensity = np.expand_dims(intensity, 2)
    retinex = np.expand_dims(retinex, 2)

    intensity1 = simplestColorBalance(retinex, low_clip, high_clip)

    intensity1 = (intensity1 - np.min(intensity1)) / \
                 (np.max(intensity1) - np.min(intensity1)) * \
                 255.0 + 1.0

    img_msrcp = np.zeros_like(img)
    
    for y in range(img_msrcp.shape[0]):
        for x in range(img_msrcp.shape[1]):
            B = np.max(img[y, x])
            A = np.minimum(256.0 / B, intensity1[y, x, 0] / intensity[y, x, 0])
            img_msrcp[y, x, 0] = A * img[y, x, 0]
            img_msrcp[y, x, 1] = A * img[y, x, 1]
            img_msrcp[y, x, 2] = A * img[y, x, 2]

    img_msrcp = np.uint8(img_msrcp - 1.0)

    return img_msrcp

run.py

import sys
import os

import cv2
import json

import retinex

data_path = 'data'
img_list = os.listdir(data_path)
if len(img_list) == 0:
    print( 'Data directory is empty.')
    exit()

with open('config.json', 'r') as f:
    config = json.load(f)

for img_name in img_list:
    if img_name == '.gitkeep':
        continue
    
    img = cv2.imread(os.path.join(data_path, img_name))

    print('msrcr processing......')
    img_msrcr = retinex.MSRCR(
        img,
        config['sigma_list'],
        config['G'],
        config['b'],
        config['alpha'],
        config['beta'],
        config['low_clip'],
        config['high_clip']
    )
    cv2.imshow('MSRCR retinex', img_msrcr)
    cv2.imwrite("MSRCR_retinex.tif",img_msrcr);


    print('amsrcr processing......')
    img_amsrcr = retinex.automatedMSRCR(
        img,
        config['sigma_list']
    )
    cv2.imshow('autoMSRCR retinex', img_amsrcr)
    cv2.imwrite('AutomatedMSRCR_retinex.tif', img_amsrcr)


    print('msrcp processing......')
    img_msrcp = retinex.MSRCP(
        img,
        config['sigma_list'],
        config['low_clip'],
        config['high_clip']        
    )    

    shape = img.shape
    cv2.imshow('Image', img)

    cv2.imshow('MSRCP', img_msrcp)
    cv2.imwrite('MSRCP.tif', img_msrcp)
    cv2.waitKey()

图像增强之python实现——MSR,MSRCR,MSRCP,autoMSRCR

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