Single image dehazing has been a challenging problem because of its ill-posed nature. For this reason, numerous efforts have been made in the field of haze removal. This paper proposes a simple, fast, and powerful algorithm for haze removal. The medium transmission is derived as a function of the saturation of the scene radiance only, and the saturation of scene radiance is estimated using a simple stretching method. A different medium transmission can be estimated for each pixel because this method does not assume that transmission is constant in a small patch. Furthermore, this paper presents a color veil removing algorithm, which is useful for an image with fine or yellow dust, using the white balance technique. The proposed algorithm requires no training, prior, and refinement process. The simulation results show that the proposed dehazing scheme outperforms state-of-theart dehazing approaches in terms of both computational complexity and dehazing efficiency.

Outdoor images in sand-dust environments play an adverse role in various remote-based computer vision tasks because captured sand-dust images have severe color casts, low contrast, and poor visibility. However, although sand-dust image restoration is as important as haze removal and underwater image enhancement, it has not been sufficiently studied. In this paper, we present a novel color balance algorithm for sand-dust image enhancement. The aim of the proposed enhancement method is to obtain a coincident chromatic histogram. First, we introduce a pixel-adaptive color correction method using the mean and standard deviation of chromatic histograms. Pixels of each color component are adjusted based on the statistical characteristics of the green component. Second, a green-mean-preserving color normalization technique is presented. However, using the mean of red and blue components as the mean of the green can result in an undesirable output because the red or blue components of many sand-dust images have a narrow histogram with a high peak. To address this problem, we propose a histogram shifting algorithm that makes the red and blue histograms overlap the green histogram as much as possible. Based on this algorithm, bluish or reddish artifacts of the enhanced image can be reduced. Finally, image adjustment is exploited to improve the brightness of the sand-dust image. We performed intensive experiments for various sand-dust images and compared the performance of the proposed method with those of state-of-the-art enhancement methods. The simulation results indicate that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.

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. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. 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. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. 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. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.