Abstract
Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.
Highlights
As with light propagating through the air, underwater light propagation is affected by scattering and absorption
This paper mainly aims at the reconstruction of underwater images which are caused due to the poor lightening and dust in the deep-water bodies
Lu et al [32] suggested a method to enhance the underwater images of shallow oceans by estimating the ambient light in an image
Summary
As with light propagating through the air, underwater light propagation is affected by scattering and absorption. While light attenuation coefficients in the air are measured in inverse kilometers, they are measured in inverse meters in an underwater environment [1]. Such severe light degradation creates significant challenges for imaging sensors attempting to acquire information about the underwater area of interest. Water is transparent to the visible spectrum but opaque to all other wavelengths. The visible spectrum’s constituent wavelengths absorb at varying rates, with longer wavelengths absorbing more rapidly
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