Underwater image enhancement using contrast correction

Abstract

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.