Showing papers by "Chongyi Li published in 2016"

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.

Single underwater image restoration by blue-green channels dehazing and red channel correction

Abstract: Restoring underwater image from a single image is know to be ill-posed, and some assumptions made in previous methods are not suitable for many situations. In this paper, we propose a method based on blue-green channels dehazing and red channel correction for underwater image restoration. Firstly, blue-green channels are recovered via dehazing algorithm based on an extension and modification of Dark Channel Prior algorithm. Then, red channel is corrected following the Gray-World assumption theory. Finally, in order to resolve the problem which some recovered image regions may look too dim or too bright, an adaptive exposure map is built. Qualitative analysis demonstrates that our method significantly improves visibility and contrast, and reduces the effects of light absorption and scattering. For quantitative analysis, our results obtain best values in terms of entropy, local feature points and average gradient, which outperform three existing physical model available methods.

Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging

Abstract: Restoring underwater image from a single image is known to be an ill-posed problem. Some assumptions made in previous methods are not suitable in many situations. In this paper, an effective method is proposed to restore underwater images. Using the quad-tree subdivision and graph-based segmentation, the global background light can be robustly estimated. The medium transmission map is estimated based on minimum information loss principle and optical properties of underwater imaging. Qualitative experiments show that our results are characterized by relatively genuine color, natural appearance, and improved contrast and visibility. Quantitative comparisons demonstrate that the proposed method can achieve better quality of underwater images when compared with several other methods.

Single underwater image enhancement based on color cast removal and visibility restoration

Abstract: Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods.

Manmade target extraction based on multistage decision and its application for change detection in polarimetric synthetic aperture radar image

Abstract: Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm.