A Survey on Underwater Image Enhancement Techniques

Abstract

This review paper deals with the methods to improve underwater image enhancement techniques, the processing of underwater image captured is necessary because the quality of underwater images affect and these image leads some serious problems when compared to images from a clearer environment. A lot of noise occurs due to low contrast, poor visibility conditions (absorption of natural light), non uniform lighting and little color variations, pepper noise and blur effect in the underwater images because of all these reasons number of methods are existing to cure these underwater images different filtering techniques are also available in the literature for processing and enhancement of underwater images one of them is image enhancement using median filter which enhances the image and help to estimate the depth map and improve quality by removing noise particles with the help of different techniques, and the other is RGB Color Level Stretching have used. Forward USM technique can also be used for image enhancement.

Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition

“…Restoring clear and real video of underwater image is of great importance to marine ecology, underwater archaeology, underwater biological recognition, and underwater robot vision [1]. However, in contrast with the images under natural environment, the underwater image is more complex and poorly visible.…”

Review of underwater image restoration algorithms

“…The algorithm also compensates for light attenuation along the propagation path. Sahu et al [20] propose a method for enhancement based on un‐sharp masking. Un‐sharp masking is subtracting an image from its blurred version, which to clearly show the details in an image.…”

Analyzing pre-processing filters sequences for underwater-image enhancement

“…They consist in the extraction of the intensity histograms in order to normalize them either through equalization or stretching establishing a desired range of values to which the pixels in the image are restricted. Methods such as contrast stretching, histogram equalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) are some examples [1], [7], [10]- [12], with CLAHE being one of the most recurrent with versions based on the RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) / HSI (Hue, Saturation, Intensity) color models [8]. The stretching of intensity values of an image is defined as [13]:…”

“…Filter-based techniques use highly efficient filtering methods to reduce noise to improve quality in UIs [3]. This noise is often a result of the acquisition process of the image, resulting in pixel values that do not represent the real intensity of the scene [10]. Within this category, methods such as homomorphic filtering, anisotropic filtering, bilateral filtering, linear filtering, median filtering, and wavelet noise reduction, among others, can be found [10]- [12], [20].…”