Image Haze Research Articles

안개 영상의 블럭 결함 제거와 변위 맵을 이용한 평가

안개 영상은 영상의 대비가 밝은 영역에 치우쳐 있기 때문에 영상의 정보를 전달하기 어렵다. 이러한 이유로 안개 제거 알고리즘이 연구되고 있다. 일반적으로 안개가 포함되기 전 상태의 영상을 획득하는 것이 어렵기 때문에 알고리즘의 성능을 평가하기 위해 결과 영상을 정성적으로 분석하였다. 본 논문에서는 영상의 변위 정보를 이용하여 안개 영상을 생성함으로써 정량적으로 오차를 비교하는 방법을 제안한다. 또한 이때 은닉 랜덤 마코프 모델(HRMF)에 기반한 기대값 최대화(EM) 알고리즘을 이용하여 블록 결함을 제거하였다. 다양한 합성영상 및 자연영상에 대하여 결과를 비교함으로써 제안한 알고리즘의 성능을 확인하였다. In the case of a haze image, transferring the information of the original image is difficult as the contrast leans toward bright regions. Thus, dehazing algorithms have become an important area of study. Normally, since it is hard to obtain a haze-free image, the output image is qualitatively analyzed to test the performance of an algorithm. However, this paper proposes a quantitative error comparison based on reproducing the haze image using a disparity map. In addition, a Hidden Random Markov Model and EM algorithm are used to remove any block artifacts. The performance of the proposed algorithm is confirmed using a variety of synthetic and natural images.

Single image haze removal via depth-based contrast stretching transform

Under the weather of haze, fog, or smoke, outdoor images show poor visibility and low contrast. Low contrast results in the difficulty for carrying out basic local feature (e.g., interest points and edges) detection algorithms, which are necessary procedures in some computer vision applications. Hence, increasing contrast of degraded images is very important since it is helpful in finding more distinct features from haze images. However, few single image haze removal methods can simultaneously achieve clear visibility, sufficiently high contrast, and simplicity. In this paper, we propose an intuitive and effective method, called the depth-based contrast stretching transform (DCST), to simultaneously obtain clear visibility and enhance contrast of a single haze gray image. The DCST stretches the contrast of haze images based on the coarse depth layers of scenes. Our method is simple and almost real time and can be extended to color images. We analyze in detail that the image stretched by the DCST has a higher local contrast than the image recovered via the physical-based model. Experiments demonstrate that images stretched by the DCST have excellent visibility and contrast compared with a few existing algorithms. Compelling performance is also presented by comparing the proposed method with other representative methods in the application of local feature detection.

Retinex Algorithm on Changing Scales for Haze Removal with Depth Map

In order to improve the traffic visibility in haze weathers, a Retinex algorithm based on the changing scale for haze removal with a depth map is proposed. It requires the haze image dark channel prior treatment to obtain the estimated depth map. Then it is according to the depth map to calculate Retinex scales for different parts of a hazy image. Finally a single scale Retinex transform is performed for each part of the image. Experimental results show that the algorithm can effectively improve the traffic visibility of hazy images without halo phenomena. Compared with the existing multi-scale Retinex algorithm MSR, it has the higher speed and better enhancement effect for the images that have greatly different scene depths.

A fast image dehazing algorithm based on negative correction

Dehazing is an important but difficult issue for image processing. Recently, many dehazing algorithms have been proposed based on the dark channel prior. However, these algorithms fail to achieve a good tradeoff between the dehazing performance and the computational complexity. Moreover, the perceptual quality of these algorithms can be further improved, especially for sky areas. Therefore, this paper firstly introduces the concept of negative correction inspired by the practical application of photographic developing and a fast image dehazing algorithm is accordingly proposed. Based on the observation of the photographic developing, we find that the contrast of images can be enlarged and their saturation can also be increased when their negative images (or reverse image) are rectified. Thus, instead of estimating the transmission map, the correction factor of negative is estimated and it is used to rectify the corresponding haze images. In order to suppress halos, a modified maximum-filter is proposed to limit the larger value of correction factor of local region. The experimental results demonstrate that the proposed algorithm can effectively remove hazes and maintain the naturalness of images. Moreover, the proposed algorithm can significantly reduce the computational complexity by 56.14% on average when compared with the state-of-the-art.

Haze Removal for a Single Image Using Adaptive Template Dark Channel Prior

In this paper, we propose an adaptive template method based on the dark channel prior. The method combines with the haze imaging model to haze removal for a single image. This method can effectively remove haze from a single input image. According to the characteristics of the image itself and the haze removal effect of the different template we divide the input image into flat region, edge region and texture region. Then, select the lager size template dispose the flat region and use midrange or minitype template dispose the edge region and texture area. Experimental results demonstrate that the proposed algorithm has very good performance for fog removal and retains the image details more effectively.

Single Remote Sensing Image Dehazing

Remote sensing images are widely used in various fields. However, they usually suffer from the poor contrast caused by haze. In this letter, we propose a simple, but effective, way to eliminate the haze effect on remote sensing images. Our work is based on the dark channel prior and a common haze imaging model. In order to eliminate halo artifacts, we use a low-pass Gaussian filter to refine the coarse estimated atmospheric veil. We then redefine the transmission, with the aim of preventing the color distortion of the recovered images. The main advantage of the proposed algorithm is its fast speed, while it can also achieve good results. The experimental results demonstrate that our algorithm produces visually appealing dehazing images and retains the very fine details. Moreover, for images containing partly clear and partly hazy areas, our algorithm can also achieve good results.

Adaptive estimation of depth map for two-dimensional to three-dimensional stereoscopic conversion

With increasing demands of three-dimensional (3D) contents, the conversion of many two-dimensional (2D) contents to 3D contents has become a focus in 3D image processing. The most important and difficult problem in 2D-to-3D conversion is the estimation of the depth map using only a single-view image. Therefore, a novel method for estimating a depth map for stereoscopic conversion is proposed in this paper. A simulated haze image generated by adding a haze veil on the input image is used to represent salient region segmentation. The 3D stereoscopic image is generated on the basis of the depth map estimated by the haze removal algorithm without any heuristic cues or user interaction. A comparative study and quantitative evaluation with some other state of the art conversion methods are carried out, which demonstrate that using the proposed approach, similar or better-quality results may be obtained.

Design of Real-time Haze Image Restoration System Based on FPGA Technology

Design of Real-time Haze Image Restoration System Based on FPGA Technology

Fast single haze image enhancement

This paper presents a new method for fast single haze image enhancement without using any extra information. The proposed approach simultaneously dehazes image and enhances sharpness by means of individual treatment of the model component and the residual. In the haze removing stage, two coarse transmission maps using dark channel prior are fused. One is obtained based on single-point pixel and the other is obtained by patch. For the sake of dehazing and enhancing sharpness simultaneously, a modified unsharp masking framework is applied to control the effectiveness of sharpness by constructing a sigmoid function adaptively. The main advantage of the proposed approach compared with others is its higher speed. This speed allows the enhanced haze image to be applied in real-time processing applications. A comparative experiment with a few other state of the art algorithms shows similar or better visual results.

전달량 보정을 통한 영상의 안개제거 개선

!edge #$%&' ()*+ ,-. /01234 &56  7.  (89:**(; ?@ABC-DE7FGHEI J%&K>LKM ()*+ $%&' #7.  #NOP56Q&RDark Channel Prior(DCP) GHEI*9S(T* !U. !>VWX$YZ&[< $%&'  47. &\ARGB ]^ !_`aVb*X cH8.$ d. 7ef 234&gh7. ,-!& GHEI J%&'23h ij$klmn/ opq!KYGHEIrs&'tj$uvwxgyzmH (h{r#:|7.AbstractIn this paper, we propose a transmission rate compensation method to remove a haze of an image by using edge information of a haze image and image segmentation. With a hazed image, it is difficult not only to recognize objects in the image but also to use an image processing method. One of the famous defogging algorithm named 'Dark Channel Prior'(DCP) is used to predict fog transmission rate using dark area of an image, and eliminates fog from the image. But there is a big possibility to calculate a wrong transmission rate if the area of high RGB values is larger than the area of the reference area. Therefore we eliminate color distortion area to calculate transmission rate by using the propose method, and obtain a natural clean image from a hazed image. Keyword : haze removal, fog, haze, dark channel prior

雾天降质图像的快速复原

Under haze,fog,and rain weather conditions,the image received by a photoelectric imaging system will lose the contrast and color fidelity owing to the absorption and scattering in atmosphere. To overcome the image degradation mentioned above,this paper proposes a new dark channel image estimation method using morphology filter based on the analysis of the atmospheric optical model and the dark channel priority. It further utilizes a dynamic parameter strategy to solve the color distortion in a large area of sky or white wall.Results on a variety of outdoor haze images show that the proposed method can effectively restore a haze-degraded image and improve the image quality. As for the image with 600 pixel × 400 pixel,the restoration simulation time by Matlab is only 0. 4 s. The dynamic parameter strategy is simple but effective to reduce the color distortion phenomenon.

Universal strategy for surveillance video defogging

We present a new approach to remove haze from surveillance video sequences. This approach extracts the background image through the frame differential method, uses the dark channel prior to estimate the atmospheric light, and then calculates a universal transmission map on the intensity component of the background image through a process of multiscale retinex, parameter adjustment, bilateral filtering, and total variation denoising filtering. Finally, it renders haze-free video according to the haze image model. The main advantage of the proposed approach is its speed as this approach adopts a "universal strategy" that applies the same atmospheric light and a universal pseudo-transmission map to a series of video frames. Experiments demonstrate that our method produces visually pleasing defogging results and tends to preserve main details better than previous techniques. A comparative study and quantitative evaluation show the efficiency of the proposed method.

Spectral change directions of multispectral subpixels in image fusion

In an image fusion process, the spatial detail in a panchromatic pixel is injected into the corresponding n-band multispectral (MS) subpixel to yield a synthetic pixel. The synthetic pixel can be regarded as the sum of three terms: the MS subpixel, a shift term, and a product of the spatial detail and an n-dimensional (n-D) spectral change vector. In this paper, the spectral change vector directions in some current image fusion methods are characterized and classified. As the spectral development of subpixels with the improvements in spatial resolution in imaging is analogous to the spectral change of MS subpixels with the improvements in spatial resolution in image fusion, the former is used as a reference to examine the spectral change vector directions in the current image fusion methods. Moreover, image haze and unmixing of mixed MS subpixels are highlighted as two aspects needing attention for further improvement in fusion quality.

Single Image Haze Removal Using Dark Channel Prior

In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.

Two improvement schemes of PAN modulation fusion methods for spectral distortion minimization

Fusion of panchromatic (PAN) and multispectral (MS) images is one of the most promising issues in remote sensing. PAN modulation fusion methods are usually based on an assumption that a ratio of two different‐resolution versions of an MS band is equal to a ratio of two different‐resolution versions of a PAN image. In such fusion methods, image haze is rarely taken into account, and it may produce serious spectral distortion in synthetic images. In this paper, assuming that the previous ratio relationship only holds for haze‐free images, two relevant improvement schemes are proposed to better express the ratio relationship of haze‐included images. In a test on a spatially degraded IKONOS dataset, the first scheme synthesizes an image with minimum spectral distortion, and the second modifies several current PAN modulation fusion methods and generates high‐quality synthetic products. The experiment results confirm that image haze can seriously impact the quality of fused images obtained by using PAN modulation fusion methods, and it should be taken into account in relevant image fusion.

Generation of Heat Haze Image Using Graphics Hardware

Generation of Heat Haze Image Using Graphics Hardware

Dust devil vortices seen by the Mars Pathfinder Camera

Discovery of dust devil vortices in Mars Pathfinder (MPF) images reveals a dust entrainment mechanism at work on Mars. Scattering of visible light by dust in the Martian atmosphere creates a pronounced haze, preventing conventional image processing from displaying dust plumes. Spectral differencing techniques have enhanced five localized dust plumes from the general haze in images acquired near midday, which we determine to be dust devils. Processing of 440 nm images highlights dust devils as distinct occultation features against the horizon. The dust devils are interpreted to be 14–79 m wide, 46–350 m tall, travel at 0.5–4.6 m/s, with dust loading of 7E‐5 kg m‐3, relative to the general haze of 9E‐8 kg m‐3, and total particulate transport of 2.2–700 kg. The vortices match predictions from terrestrial analog studies.

Experimental Transfer Characteristics of Image Motion and Air Conditions in Aerial Photography

Factors that impair the quality of aerial photographs include image movement, air turbulence, and haze. The effects of image movement are treated theoretically and experimentally. In the experimental study, data have been obtained by photographing a pattern on one airplane from another flying above it. The effects of the atmosphere can be separated into two types. One, due to haze, reduces contrast in a manner independent of spatial frequency; the other, attributed to air turbulence, has a spatial-frequency dependence and is treated as an apparent “aberration” of the optical system.