Underwater image quality enhancement through integrated color model with Rayleigh distribution
Method to increase the contrast and reduce the noise of underwater image.Applied histogram modification of integrated RGB and HSV color models.Mapping the image histogram according to Rayleigh distribution.Limiting the dynamic range of color models to reduce under and over enhanced areas.Outperforms other state of the art methods in term of contrast and noise reduction. The physical properties of water cause light induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red Green Blue (RGB) and Hue Saturation Value (HSV) In the RGB color model, the histogram of the dominant color channel (i.e. blue channel) is stretched toward the lower level, with a maximum limit of 95% whereas the inferior color channel (i.e. red channel) is stretched toward the upper level, with a minimum limit of 5% The color channel between the dominant and inferior color channels (i.e. green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue green effect, and minimizes under and over enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state of the art methods.
Appl. Soft Comput.