Image Enhancement

Introduction

Visibility in underwater images is usually poor because of the attenuation of light in the water that causes low contrast and color variation. In this , a new approach for underwater image quality improvement is presented,based on this paper. The proposed method aims to improve underwater image contrast, increase image details, and reduce noise by applying a new method of using contrast stretching to produce two different images with different contrasts.

Mehodology

Here we are dividing a image from its average pixel value to highest & lowest respectively. Calling it as high contrast & low contrast respectively.Followed by this Rayleigh stretching of each pixel over its entire range. We get two images till now, we use their extremities to get an average image with averaging the noises. The flow of process goes like this:

Input Image

1] Color channel decomposition

   b,g,r=cv2.split(img2)
   b1,g1,r1=cv2.split(img1)

2] Finding average pixel

First we split down the image in its respective b,g,r channel. We need to find average pixel for each channel.

   imdb = ((b2.max()-b2.min())/2) + b2.min()
   imdg = ((g2.max()-g2.min())/2) + g2.min()
   imdr = ((r2.max()-r2.min())/2) + r2.min()

3] Rayleigh stretching Its based on Rayleigh Distribution algorithm. Here using the basic principle of stretching the pixels over entire range,as mentioned in eq2. Integrating this equation results in dynamic stretching of pixels over the entire range as shown in eq4. Here Rayleigh stretching of 'b' channel for high contrast:

   b[b<imdb] = imdb
   for index,value in np.ndenumerate( b ):
       new_value = ((255* (value-imdb))/ ((b2.max()-b2.min())/(alpha**2)))
        b[index] = new_value

4] Image composition We merges both set of channel to obtain high contrast & low contrast images:

    res=cv2.merge((b,g,r))
    res1=cv2.merge((b1,g1,r1))

5] Averaging two images, here we can use diffetent methods of averaging images, but the simplest for two images is weighted addition:

    res2= cv2.addWeighted(res,.5,res1,.5,0)

6] Final stage is to increse the contrast of the image, there are no. of ways from which we can do this, like stretching s & v component image over entire range, gamma correction, histogram equalization.We need to maintain image for segmentation so we are omitting histogram stretching, instead we uses gamma correction:

    def adjust_gamma(image, gamma=1.0):
        invGamma = 1.0 / gamma
          table = np.array([((i / 255.0) ** invGamma) * 255
	        for i in np.arange(0, 256)]).astype("uint8")
        return cv2.LUT(image, table)