Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior

Abstract

Images captured under water are usually degraded due to the effects of absorption and scattering. Degraded underwater images show some limitations when they are used for display and analysis. For example, underwater images with low contrast and color cast decrease the accuracy rate of underwater object detection and marine biology recognition. To overcome those limitations, a systematic underwater image enhancement method, which includes an underwater image dehazing algorithm and a contrast enhancement algorithm, is proposed. Built on a minimum information loss principle, an effective underwater image dehazing algorithm is proposed to restore the visibility, color, and natural appearance of underwater images. A simple yet effective contrast enhancement algorithm is proposed based on a kind of histogram distribution prior, which increases the contrast and brightness of underwater images. The proposed method can yield two versions of enhanced output. One version with relatively genuine color and natural appearance is suitable for display. The other version with high contrast and brightness can be used for extracting more valuable information and unveiling more details. Simulation experiment, qualitative and quantitative comparisons, as well as color accuracy and application tests are conducted to evaluate the performance of the proposed method. Extensive experiments demonstrate that the proposed method achieves better visual quality, more valuable information, and more accurate color restoration than several state-of-the-art methods, even for underwater images taken under several challenging scenes.

An Underwater Image Enhancement Benchmark Dataset and Beyond

“…According to the findings that the background color of underwater images has relations with the inherent optical properties of water medium, Zhao et al [43] enhanced the degraded underwater images by deriving inherent optical properties of water from the background color. Li et al [44], [45] proposed an underwater image enhancement method based on the minimum information loss principle and histogram distribution prior. Peng et al [46] proposed a depth estimation method for underwater scenes based on image blurriness and light absorption, which is employed to enhance underwater images.…”

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

“…We compare the proposed model with several state-of-theart methods: image-to-image transfer method (i.e., CycleGAN [29]), color constancy method (i.e., Gray Word (GW) [23] ), image enhancement method (i.e., INT [39]), and underwater image restoration methods (i.e., RED [9], UWID [11] and UWIB [12]). In our experiments, the test images were captured under varying underwater scenes.…”

A Review: Underwater Image Enhancement using Dark Channel Prior with Gamma Correction

“…The absorption and scattering particles present in the water causes haze in the image captured by the camera. The commonly used image formation model [8] is shown in figure 2. According to computer vision application, captured hazy image ( ) is represented as, ( ) = ( ) ( ) + (1 − ( )) (1) In above equation (1), ( ) is the intensity of the foreground or haze-free image, ( ) is medium transmission map representing percentage of residual energy when foreground light passes through the medium which is represented as,…”

“…John Y. Chiang and Ying-Ching Chen implemented a wavelength compensation and image dehazing (WCID) [8] algorithm to remove the distortions caused by light change and color change simultaneously. They also used the dark channel prior method to estimate the distance of the scene objects to the camera, called as depth map.…”

“…Based on the observation that the dark and bright regions of underwater images become too dark or too bright after being restored by this dehazing algorithm, so an adaptive exposure map is employed to adjust the results for better visual quality [8]. The adaptive exposure map ( ) can be obtained by solving the following optimization problem:…”