This method achieved improved visual quality of underwater images by enhancing contrast and reducing noise and artifacts. CLAHE was applied to the RGB and HSV color models to generate two images. adjusted contrast limited adaptive histogram equalization (CLAHE) and built mixed CLAHE to improve the visibility of underwater images.
Red colorcast underwater images series#
Their algorithm was based on a series of stretching, such as contrast stretching in RGB space, and saturation and brightness stretching in the HSI space. proposed an underwater image-enhancement method using an integrated color model. These methods use various image processing techniques applied to natural images, and include histogram stretching, Retinex, color correction, and fusion-based algorithms. Model-free underwater image-enhancement algorithms aim at improving the contrast and color of images without any underwater imaging model. Therefore, enhancing underwater images is a challenging and important task. In the underwater environment, color correction is a difficult task because the distortion of color occurs asymmetrically depending on the wavelength of light. Low-quality underwater images may cause failures in computer vision applications such as inspection, environmental sensing, object detection, and object recognition. Because red light has a longer wavelength, most underwater images look bluish or greenish. The second factor is the attenuation of light, which depends on the optical wavelength, dissolved organic compounds and water salinity, which causes various color casts. The first factor is that reflected light from the underwater object is absorbed and scattered by particles suspended in water, which lowers the image contrast and produces a hazy effect in the underwater image.
Two major factors lead to the degradation of underwater images. Therefore, images captured under water have a reduced contrast and hazy effect. Light is attenuated due to the complicated underwater environment and lighting conditions when it propagates through water. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. The corrected image based on this color balance algorithm barely produces a reddish artifact. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image.
Thus, numerous efforts have been made in the field of underwater image restoration. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium.
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