Fog recovery problem in image defogging technology

The fog recovery problem in image defogging technology requires specific code examples

With the continuous development of computer vision technology, image defogging technology has gradually been widely used. Under normal photography conditions, the presence of fog often leads to problems such as reduced image quality and loss of details. Therefore, how to restore fog in images has become one of the hot topics of research.

Generally speaking, the goal of image dehazing is to restore the original haze-free image by estimating and removing fog scattering from the hazy image. The core problem of image dehazing is how to accurately estimate fog.

At present, image dehazing technology mainly includes two methods: single image dehazing and multiple image dehazing. Single image defogging refers to directly defogging a foggy image, while multi-image defogging involves defogging images from multiple perspectives or time series.

In single image dehazing, the most commonly used method is to use the atmospheric scattering model to estimate fog. The atmospheric scattering model describes the scattering and absorption of light by fog, as follows:

I = J t A (1 - t)

where, I is Measured image, J is the original fog-free image, A is the global atmospheric illumination, and t is the fog concentration. The goal of image dehazing is to recover J by estimating t and A.

Of course, the atmospheric scattering model assumes that light is uniform throughout the entire scene and that the fog concentration is globally uniform. However, in real-life scenarios, these assumptions often do not hold. Therefore, researchers have proposed many improved algorithms to deal with these problems.

The following is a specific code example, showing an image dehazing method based on dark channel prior:

import numpy as np
import cv2

def dark_channel(img, patch_size):
  min_channel = np.min(img, axis=2)
  return cv2.erode(min_channel, np.ones((patch_size, patch_size)))

def atmospheric_light(img, dark_img, top_percentage):
  h, w = img.shape[:2]
  flattened_img = img.reshape(h*w, 3)
  flattened_dark = dark_img.flatten()
  top_num = int(h*w*top_percentage)
  indices = np.argpartition(flattened_dark, -top_num)[-top_num:]
  top_pixels = flattened_img[indices]
  atmospheric_light = np.max(top_pixels, axis=0)
  return atmospheric_light

def transmission_map(img, atmosphere_light, omega, patch_size):
  img_normalized = img / atmosphere_light
  dark = dark_channel(img_normalized, patch_size)
  transmission = 1 - omega * dark
  return transmission

def recover(img, transmission, atmosphere_light, omega):
  transmission_normalized = np.maximum(transmission, omega)
  recover = (img - atmosphere_light) / transmission_normalized + atmosphere_light
  return np.clip(recover, 0, 255).astype(np.uint8)

def dehaze(img, omega=0.95, patch_size=15, top_percentage=0.001):
  dark = dark_channel(img, patch_size)
  atmospheric_light = atmospheric_light(img, dark, top_percentage)
  transmission = transmission_map(img, atmospheric_light, omega, patch_size)
  output = recover(img, transmission, atmospheric_light, omega)
  return output

if __name__ == '__main__':
  img = cv2.imread('hazy_image.jpg')
  output = dehaze(img)
  cv2.imwrite('dehazed_image.jpg', output)

This code implements an image based on dark channel prior Dehazing method. Through the dark channel prior, the atmospheric illumination and transmittance in the image can be estimated. Then, the haze-free image is restored by calculating the reverse transmittance.

Of course, this is just an example of one method, and there are many ways to dehaze images. There are many more in-depth studies and specific implementations of image dehazing, and readers can further explore and understand according to their needs and interests.

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