Image Haze Removal Research Articles

Underwater visibility restoration using dehazing, contrast enhancement and filtering

A framework for underwater image haze removal and visibility enhancement is proposed. Hybrid median filter in conjunction with shock filter is utilized for initial enhancement of image. For further enhancement and color restoration, content adaptive detail enhancement is applied followed by contrast enhancement in the RGB and HSV color space. The proposed framework effectively dehazes the images, and also minimizes color distortion and artifacts. The quantitative and visual analysis effectiveness demonstrate the effectiveness of our proposed technique.

Image Haze Removal Using Airlight White Correction, Local Light Filter, and Aerial Perspective Prior

Light is scattered and absorbed when travelling through atmosphere particles, leading to visibility attenuation for images captured, especially in hazy scenes. In addition, hazy images may suffer from color distortion caused by haze or sandstorm, resulting in a poor visual quality. In order to effectively enhance visibility and correct possible color casts for such images, we propose a new image dehazing algorithm based on an improved haze optical model, which consists of three modules: airlight white correction (AWC), local light filter (LLF), and aerial perspective prior (APP). In the proposed algorithm, the AWC module detects and corrects possible color cast, the LLF module downplays non-hazy bright pixels (e.g., headlight and white objects) for more accurate airlight estimation, and the APP module uses the minimum/maximum channel and their difference for scene transmission estimation. The experimental results demonstrate that the proposed method outperforms other state-of-the-art dehazing methods in three ways: 1) our results have better visual quality; 2) our method performs the best in terms of color restoration; and 3) our method is very efficient at removing haze and color casts.

Cardinal color fusion network for single image haze removal

Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous approaches, DehazeNet (Cai et al. in IEEE Trans Image Process 25(11):5187–5198, 2016), MSCNN (Ren et al., in: European conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the first stage, it generates multi-channel depth maps using a novel parallel convolution filter bank. The second stage comprises of multi-scale convolution filter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution filter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on image processing (ICIP), 2016), ImageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the effectiveness of the proposed approach. Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for single image haze removal. Further, the effect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that the proposed network estimates robust TrMap and recovers haze-free scene without color distortion.

Image Dehazing Technique Based On DWT Decomposition and Intensity Retinex Algorithm

Conventional designs use multiple image or single image to deal with haze removal. The presented paper uses median filer with modified co-efficient (16 adjacent pixel median) and estimate the transmission map and remove haze from a single input image. The median filter prior(co-efficient) is developed based on the idea that the outdoor visibility of images taken under hazy weather conditions seriously reduced when the distance increases. The thickness of the haze can be estimated effectively and a haze-free image can be recovered by adopting the median filter prior and the new haze imaging model. Our method is stable to image local regions containing objects in different depths. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly. Our method due to its fast speed and the good visual effect is suitable for real-time applications. This work confirms that estimating the transmission map using the distance information instead the color information is a crucial point in image enhancement and especially single image haze removal.

Fast Single Image Haze Removal Scheme Using Self-Adjusting

At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness factor to be a ratio of minimum to maximum pixel intensity of the hazy image. Adaptive contrast, extinction coefficient, the maximum visible distance of hazy images as well as dehazed images from each image are evaluated uniquely. The resulting high-quality haze free image with linear computational complexity O(n) is appropriate for real time applications. The effectiveness of the technique is validated by quantitative, and qualitative evaluations.

Single Image Haze Removal Technique via Pixel-based Joint BDCP and Hierarchical Bilateral Filter

Single Image Haze Removal Technique via Pixel-based Joint BDCP and Hierarchical Bilateral Filter

Joint Transmission Map Estimation and Dehazing using Deep Networks

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function. Extensive experiments on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

An Efficient Image Haze Removal Algorithm based on New Accurate Depth and Light Estimation Algorithm

Single image Dehazing has become a challenging task for a variety of image processing and computer applications. Many attempts have been devised to recover faded colors and improve image contrast. Such methods, however, do not achieve maximum restoration, as images are often subject to color distortion. This paper proposes an efficient single image Dehazing algorithm that offers satisfactory scene radiance restoration. The proposed method stands on the estimation of two key indices; image blur and atmospheric light that can be employed in the Image Formation Model (IFM) to recover scene radiance of the hazy image. More clearly, we propose an efficient depth estimation method using image blur. Most existing algorithms implement atmospheric light as a constant which often leads to inaccurate estimations, we propose a new algorithm “A-Estimate” based on blur and energy to estimate the atmospheric light accurately, an adaptive transmission map also has been proposed. Experimental results on real and synthesized hazy images demonstrate an improved performance in the proposed method when compared to existing state-of-the-art methods.

Hardware Implementation for Haze Removal With Adaptive Filtering

Haze removal is useful in computational photography and computer vision applications. Single image haze removal is still a challenge for real-time embedded systems. In general, dark channel prior approach can effectively remove haze from a single image. However, the halo effect is the major problem of dehazing. In this study, adaptive filtering, which is easy for VLSI design, is introduced to refine the transmission map and improve the result of haze removal. A hardware architecture for our haze removal method is proposed to achieve the real-time requirement. A specific hardware component is designed to compute lengths of the paths and adaptive structuring element (ASE). The hardware architecture for haze removal is implemented in 180 nm CMOS technology and occupies 5.46 mm 2 with 143 K bit on-chip memory. The design can operate at 177MHz and support for HD (1280×720) 192 frame/s. Experiment results show that the hardware implementation is suitable in embedded system for real-time HD applications.

Single image haze removal with approximate radiance darkness prior

In this paper, we propose a novel and effective method for removing haze based on a single image, which firstly computes the dark channel of the estimated radiance image by decomposing the dark channel of the haze input image, and the method then estimates the transmission map of the input image. Finally, the scene radiance image is restored by the classical atmospheric scattering model. Experimental results show that the proposed method outperforms He et al.’s method in terms of haze removal.

Joint learning of image detail and transmission map for single image dehazing

Single image haze removal is an important task in computer vision. However, haze removal is an extremely challenging problem due to its massively ill-posed, which is that at each pixel we must estimate the transmission and the global atmospheric light from a single color measurement. In this paper, we propose a new deep learning-based method for removing haze from single input image. First, we estimate a transmission map via joint estimation of clear image detail and transmission map, which is different from traditional methods only estimating a transmission map for a hazy image. Second, we use a global regularization method to eliminate the halos and artifacts. Experimental results on synthetic dataset and real-world images show our method outperforms the other state-of-the-art methods.

Superpixel-Based Single Nighttime Image Haze Removal

Haze removal is important to improve performance of outdoor vision systems. However, it is challenging to remove haze from a single nighttime haze image. In this paper, a novel superpixel-based single image haze removal algorithm is proposed for nighttime haze images. The input nighttime image is first decomposed into a glow image and a glow-free nighttime haze image using their relative smoothness. A superpixel-based method is then introduced to compute the value of the atmospheric light and dark channel for each pixel in the glow-free haze image. The transmission map is decomposed from the dark channel of the glow-free haze image by the weighted guided image filter. Since superpixels usually adhere to the boundaries of objects well, a smaller local window size can be selected. As such, details in areas of fine structures are preserved better. In addition, to avoid noticeable noise in the sky area, an adaptive threshold is added to the transmission map when the nighttime haze image is restored. Experiments show that our method produces better results than the existing haze removal algorithms for nighttime haze images.

Image Processing Technology in Fog and Haze Environment Based on Retinex Theory

For the multi-scale Retinex algorithm (MSR), the image contrast and image information is not ideal enough. In this paper, we propose an algorithm of image haze removal based on the fusion of dark prior and Retinex theory. Firstly, the algorithm of dark primary color is used to restore the haze image. Then, the multi-scale Retinex algorithm is applied to enhance the haze. Finally, the average value of brightness, standard deviation, entropy, mean square error, and peak signal-to-noise ratio were used as evaluation criteria for image enhancement. The simulation results of Matlab software show that the smog image processed by this algorithm has increased image contrast and more image information. We conduct experiments to prove that the proposed algorithm can provide a better representation.

Image Haze Removal via Reference Retrieval and Scene Prior.

Photography of hazy scene typically suffers from low-contrast which degrades the visibility of the scene. The performance of single-image dehazing methods is limited by the priors or constraints. In this paper, we present an effective method for haze removal, which utilizes its retrieved correlated haze-free images as external information. The correlated haze-free images are with scene prior offering scene structure and local high frequency information for dehazing, although variations in viewpoints, scales, and illumination conditions exist. To utilize those reference more effectively, global geometric registration and local block matching toward the hazy input are performed to reinforce the spatial correlations. Based on the registration, different kinds of external information are estimated. In addition, we combine that additional external information with internal constraint and regularization for estimating scene transmission map. Experiments demonstrate that our approach can produce dehazing results with better visual quality compared with other state-of-the-art methods.

Single Image Haze Removal with Improved Atmospheric Light Estimation

A novel method for atmospheric light estimation has been proposed in this paper. The new estimation method, called Estimation Based on Patch Size Adjustment (PSA), firstly segments the sky region of dark channel through threshold segmentation method and then adaptively adjusts the patch for dark channel until the estimated atmospheric light is right in the segmented sky region. Experimental results show that this method is effective even when the intensity of some scene objects is inherently higher than the atmospheric light and no shadow is cast near them. However, PSA spends a lot of time on adjustment. In order to improve the time efficiency, another simplified method, called Estimation Based on Sky Region Segmentation (SRS), is proposed by directly estimating the atmospheric light in the segmented sky region. The test results show SRS is almost as good as PSA on haze removal effect. Both methods have advantages on improving visual effect and objective indicators of the haze-free images.

Single Image Haze Removal: Comparative Studies with Advanced Matting Approaches

It is widely known that the existence of haze in the atmosphere reduces the quality of images, which are captured by sensors of camera. The haze or fog removal, called dehazing or defogging, is usually completed according to the model of physical degradation, which requires the solution of poorly posed inverse problem. To alleviate the difficulty of the inverse problem, the dark channel prior (DCP) was previsouly proposed and received much attention. In this paper, a DCP-based dehazing of a single input image is proposed. Considering the concerns raised in matting technology, we have replaced it by more advanced technologies, which manage the task more appropriately. After a series of experiments we decided to combine two methods forming an estimated semi-automatic algorithm for finding the corresponding values. The results on various foggy images show the strength of the proposed new algorithm.

Saliency‐based dark channel prior model for single image haze removal

Images degraded by haze usually have low contrast and fide colours, and thus have bad effects on applications such as object tracking, face recognition, and intelligent surveillance. So the purpose of dehazing is to recover the image contrast without colour distortion. The dark channel prior (DCP) is widely used in the field of haze removal because of its simplicity and effectiveness. However, when faced with bright white objects, DCP overestimates the haze from its true value and thus causes colour distortion. In this study, the authors propose a dehazing model combining saliency detection with DCP to obtain recovered images with little colour distortion. There are three main contributions. First, they introduce a novel saliency detection method, focusing on superpixel intensity contrast, to extract bright white objects in the hazy image. Those objects are not used to estimate the atmospheric light and transmission in the dark channel image. Second, a self-adaptive upper bound is set for the scene radiance to prevent some regions being too bright. Third, they propose a quantitative indicator, colour variance distance, to evaluate the colour restoration. Experimental results show that their proposed model generates less colour distortion and has better comprehensive performance than competing models.

Single Image Haze Removal Method Using Conditional Random Fields

In this letter, we propose a single image haze removal method, which is inherently an ill-posed problem due to the transmission estimation that is dependent on the depth information. The pointwise haze density estimation method using conditional random fields includes the unary factor that encodes the likelihood for the haze density and the pairwise factor that encodes the spatial contextual relationship between adjacent unary factors. The proposed scheme makes elimination of the refining process possible and it may prevent the halo effect at object boundaries. The results of experiments carried out on various real and synthesized images, and comparison with existing methods are presented to verify the effectiveness of the proposed method.

Saliency-driven single image haze removal method based on reliable airlight and transmission

Haze removal has become an attractive topic in recent years and several dehazing methods are proposed. Dark channel prior (DCP) is one of the most effective dehazing approaches. However, when dealing with images containing large white objects, DCP often mistakes white objects for opaque haze. It will cause the airlight to be overestimated and the transmission to be underestimated, and thus the dehazing results have serious color distortion. In view of the above problem, saliency detection is introduced into haze removal to obtain better restored images in this paper. We first propose a method for reliable airlight estimation. Then, a saliency prior is presented for hazy images, which can distinguish white objects from dense haze by saliency detection. On the basis of saliency prior, both accurate airlight and a correct transmission map can be obtained from images containing large white objects, and finally these images can be restored successfully. The experimental results illustrate that our proposed method has great superiority in color recovery compared with other state-of-art methods when dealing with images containing large white objects.

Color Attenuation Prior (CAP) for Single Image Dehazing

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.