Transmission Map Estimation Research Articles

MODIFIED DARK CHANNEL PRIOR Modified DCP for Dehazing

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model, which requires a solution of an ill-posed inverse problem. In this paper we will discuss and do the comparative study of Adaptive Histogram Equalization (AHE), Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (modified DCP). This article suggest image and video image defogging algorithm working on dark channel prior technique. The modified DCP is derived from the characteristic of natural outdoor images that the intensity value of at least one color channel within a local window is close to zero. The modified DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done through four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This four step solution of modified DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc

Image smog restoration using oblique gradient profile prior and energy minimization.

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.Electronic Supplementary MaterialSupplementary material is available in the online version of this article at 10.1007/s11704-020-9305-8.

Single Image Haze Removal with Pixel-based Transmission Map Estimation

Adversary imaging condition such as in a hazy weather is a challenge in the community of image processing. To address the challenge of haze removal, several model-based approaches have been reported recently. Among them, a single image haze removal scheme based on dark channel prior (DCP) was presented in [1] and has been getting popular because of its satisfactory performance for most of cases. However, it is well-known that the DCP scheme is inherent of two fundamental problems: high computational cost and over-exposure. In this paper, the objective of proposed dehazing algorithm (PDA) is to deal with the two problems in [1], where atmospheric light is estimated from the dark channel and pixel-based transmission map estimation is employed. Examples are given to verify the PDA where comparison with the DCP scheme and other well-known schemes are made as well. The simulation results indicate that the PDA has similar or better dehazing performance than the compared schemes in the given examples, when both mood retention and visibility improvement are considered.

Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.

An intelligent framework for transmission map estimation in image dehazing using total variation regularized low-rank approximation

The presence of haze affects a multitude of applications that require detection of image features, such as target tracking, object recognition and camera-based advanced driving assistance systems. In this paper, an optimization framework is proposed to efficiently estimate the scene transmission map which aids the dehazing process in an effective manner. In the formulated optimization model, low-rank approximation using weighted nuclear norm minimization is introduced to smoothen the coarse transmission map obtained from hazy data in order to avoid the visual artifacts in the dehazed image. Total variation regularization is employed to preserve the prominent edges and salient structural details in the transmission map. Moreover, the inclusion of $$l_1$$ norm minimization helps to obtain a finer transmission map by enhancing the minute sparse structural details, thereby providing good dehazing results. The beauty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map with fine-tuned regularization terms which is not reported until now in the direction of image dehazing. The extensive experiments prove that the proposed method surpasses the state-of-the-art methods in image dehazing.

Joint transmission map estimation and image dehazing using dual vision attention network

Joint transmission map estimation and image dehazing using dual vision attention network

Improved image defogging method based on adaptive neighborhood window size of PMS network

Aiming at the failure of prior theory of dark channel in bright area, an improved algorithm based on PMS-Net is proposed. First, the selection of atmospheric light in dark channel images under different neighborhood windows is optimized, and the atmospheric light value is accurately estimated. Then an improved transmittance estimation method is used to make the transmission map estimation of the bright area more accurate. The improved PMS-Net defogging algorithm is compared with the existing excellent algorithms, and the experimental results show that the algorithm can overcome the color distortion of existing algorithm in processing bright areas. The effectiveness of the algorithm is verified by objective evaluation indicators.

Single image dehazing via atmospheric scattering model-based image fusion

Scene visibility in outdoor images is often deteriorated by bad weather conditions such as snow, haze, and rain. Especially, degradation due to haze is typically observed in the form of faded color and low contrast of images. To overcome image degradation, dehazing algorithms based on the atmospheric scattering model employ transmission map estimation, which is related to the haze density across the depth of scenes. However, estimating the depth of the outdoor scene without additional information is challenging, and an erroneously estimated depth leads to a dehazed image of poor quality. In this paper, we propose a fusion-based dehazing algorithm that does not require direct estimation of the transmission map. The intermediate latent images are obtained by restoring the scene radiance with several globally constant transmission values based on the atmospheric scattering model. Using this approach, only the region where the particular transmission corresponds to the ground truth is dehazed successfully in each image. These images are merged into a haze-free result via the fusion algorithm, which selectively uses the information of the patches from the latent images. Experimental results show that the proposed algorithm effectively removes haze and outperforms the several dehazing methods based on quantitative and qualitative evaluations.

A Real Time Haze Removal & Mosaicing u sing Rapid Prototype Hardware

In this paper, a pivotal technique was proposed that reduces the haze and combines the haze free image to increase the Field of View (FoV) in real-time with a rapid prototype hardware device. The Initial focus is to reduce the haze in an image with Dark Channel Prior Technique and the FSD method is utilized to mosaic the haze free images. Low contrast may occur due to the scattering light, air particles or fog in nature which results in a haze image that needs to be reduced and enhance the image for better vicinity. Haze reduction approach depends on entropy and information fidelity. Our Haze free algorithm executes multiple phases such as dark channel prior computation, estimation and refinement of transmission map and restoration of RGB values. The second technique is the mosaic process that improves the field of view of a scene and the phases that execute are corner detection, extraction, geometric computation and blending. Our experimental results have shown better when compared to the other algorithms. The whole process is executed in real-time with a standalone device called Intel compute stick.

Dehazing Effects on Image and Videousing AHE, CLAHE and Dark Channel Prior

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outdoor that the value of intensity inside the local window is nearly equal to zero. The DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done using following four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This solution of four step DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc.

Single image desmogging using oblique gradient profile prior and variational minimization

An efficient estimation of transmission map for desmogging model is an ill-posed problem. The quality of restored image depends upon the accurate estimation of transmission map. However, transmission map obtained using various dehazing models is not accurate in case of images with large haze gradient, and fail while image desmogging. As a result, the restored images suffer from numerous issues such as halo and gradient reversal artefacts, edge and texture distortion, color distortion, etc. Therefore, this paper designs a novel transmission map estimation by using weighted integrated transmission maps obtained from foreground and sky regions. Additionally, transmission map is further refined using an integrated variational regularized model with hybrid constraints. However, the proposed technique suffers from hyper-parameters tuning issue, therefore, in this paper, a non-dominated sorting genetic algorithm is also used to tune the hyper-parameters of the proposed technique. The comparison of designed desmogging model is also done with other dehazing models by considering benchmark and real-time hazy images. The comparative analyses reveal that the designed model outperforms existing models subjectively and quantitatively.

Accurate estimation of transmission maps for image restoration based on polarimetric parameters and average intensity

Haze limits visibility and degrades image contrast, and images lose color fidelity owing to the scattering and absorption of light caused by particles. Consequently, removing haze is critical for enhancing the image visibility and quality, with estimation of airlight and transmission map being critical steps. We propose a simple and convenient improved scheme, which is polarization-based method for dehazing. The method uses four polarization orientations (0°, 45°, 90°, 135°) to measure the Stokes vector, and obtained the degree of polarization (DOP) of the airlight by angle of polarization (AOP) in three channels. Subsequently, estimating the airlight at infinity by average total intensity and re-constructing a matrix to estimate the transmission maps. Experimental results demonstrate that a high-quality transmission map can be obtained, and this improved method can restore outdoor scene haze images with high quality and contrast. In addition, this method also proved to be useful for enhancing color saturation and restoring color fidelity.

Improved color attenuation prior based image de-fogging technique

Single image de-fogging has been a confronting problem due to its ill-posed nature. In this paper, a de-fogging technique based upon color attenuation prior (CAP) has been proposed. CAP uses linear model and learning the parameters of this model with a supervised learning method for estimating the scene depth of a foggy image. This depth map is utilized for transmission map estimation. Transmission (t) is one of the important parameter of physical model based de-fogging techniques which describes the portion of the light coming from the scene point that is not scattered and reaches the camera. It is a map called transmission or transparency of the fog 0 < t(x) < 1, t(x) = 0 means completely foggy, t(x) = 1 means fog-free. The more accurately the transmission or depth is estimated, the better the defogging performance will be. In the proposed work, to quickly and accurately estimate the transmission map, a sub-sampling based local minimum operation and fast gradient domain guided image filtering (GDGF) is applied on CAP based initial depth map. The edge attentive restraints of GDGF make edges to be conserved better in the de-fogged images. The de-fogged images obtained by CAP technique suffer from dullness and higher illumination variations due to consideration of fog image degradation model in homogeneous environment and a constant value of atmospheric light. Such variations are removed in the proposed work by using Lambert’s law of illumination reflection, which helps to compensate non uniform illumination, causes simultaneous dynamic range modification, color consistency, and lightness rendition without producing the artifacts in a de-fogged image. To improve the processing speed, image sub-sampling mechanism is used in various steps of image de-fogging. The sub-sampling is used in such a way that the quality of the output at any step is not compromised as demonstrated through various quality parameters. Experimental results show that the proposed approach outperforms state-of-the-art fog removal techniques in terms of efficiency and the de-fogging effect.

Visual Haze Removal by a Unified Generative Adversarial Network

Existence of haze significantly degrades visual quality and hence negatively affects the performance of visual surveillance, video analysis, and human–machine interaction. To remove haze from a visual signal, in this paper, we propose a generative adversarial network for visual haze removal called HRGAN. HRGAN consists of a generator network and a discriminator network. A unified network jointly estimating transmission maps, atmospheric light, and haze-free images (called UNTA) is proposed as the generator network of HRGAN. Instead of being optimized by minimizing the pixel-wise loss, HRGAN is optimized by minimizing a novel loss function consisting of pixel-wise loss, perceptual loss, and adversarial loss produced by a discriminator network. Classical model-based image dehazing algorithms consist of three separate stages: 1) estimating transmission map; 2) estimating atmospheric light; and 3) restoring haze-free image by using an atmospheric scattering model to process the transmission map and atmospheric light. Such a separate scheme is not guaranteed to achieve optimal results. On the contrary, UNTA performs transmission map estimation and atmospheric light estimation simultaneously to obtain joint optimal solutions. The experimental results on both synthetic and real-world image databases demonstrate that HRGAN outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.

Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.

Single Image Numerical Iterative Dehazing Method Based on Local Physical Features

To address the hazy image degradation problem, we introduce a single image numerical iterative dehazing method based on local physical features. The method involves three components: region division based on haze density, local atmospheric light estimation and transmission map estimation, and recovery of hazy image scene radiance by using an iterative algorithm. Because of the nonuniform haze density within an image, we first employ the affinity propagation (AP) clustering algorithm to divide a hazy image into different haze density regions. Second, to reflect the difference in atmospheric light among regions and avoid the generation of halo artifacts in recovered images, we estimate the local atmospheric light in each region to replace the global atmospheric light and then estimate the transmission via a dark channel prior. Finally, an iterative dehazing algorithm, which can be used to not only further optimize local atmospheric light and transmission but also remove haze completely, is developed based on a physical model. Experimental results illustrate that our method can effectively improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.

Evolution of New Integrated Haze Removal Algorithm Based on Haze Line

Haze is a standout amongst the most essential factor that debases the outside pictures. Picture corruption relies upon the separation of the Object from the camera as the separation increment from the camera of the scene point haze additionally increases. This paper presents a haze removal technique which is relies on the Haze-line prior that is recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air- light proposed approach is known as haze line. In this paper we proposed Post processing CBMG (Contrast, Brightness, Midtone and Gama adjustment) followed by four stages: clustering the pixel into haze line, Estimation of Transmission map, Regularization, Dehazing, This method is implement by observation of two basic things first pixels in a given group are regularly non local . Second pictures with improved perceivability (or sunny morning picture) have more differentiation than the pictures stopped by terrible climate. Experimental result demonstrate that our method remove haze layer and provide qualitative result.

RYF-Net: Deep Fusion Network for Single Image Haze Removal.

Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based architecture to estimate the TrMap of the hazy scene. The proposed network takes the hazy image as an input and extracts the haze relevant features using proposed RNet and YNet through RGB and YCbCr color spaces respectively and generates two TrMaps. Further, we propose a novel TrMap fusion network (FNet) to integrate two TrMaPs and estimate robust TrMap for the hazy scene. To analyze the robustness of FNet, we tested it on combinations of TrMaps obtained from existing state-of-the-art methods. Performance evaluation of the proposed approach has been carried out using the structural similarity index, mean square error and peak signal to noise ratio. We conduct experiments on five datasets namely: D-HAZY ancuti2016d, Imagenet deng2009imagenet, Indoor SOTS li2017reside, HazeRD zhang2017hazerd and set of real-world hazy images. Performance analysis shows that the proposed approach outperforms the existing state-of-the-art methods for single image dehazing. Further, we extended our work to address high-level vision task such as object detection in hazy scenes. It is observed that there is a significant improvement in accurate object detection in hazy scenes using proposed approach.

Dehazing for images with sun in the sky

Image dehazing is one of the most important image processing methods and it is widely used in daily application. A large number of dehazing algorithms have been proposed in recent years. In order to solve the problem that the dehazing performances of the classic dark channel prior methods for hazy images with the sun are bad, we propose a dehazing method for removing mist interference in images with the sun in the sky. An atmospheric scattering model is proposed to estimate the scattered light of the sun in the hazy circumstance. We also propose a gradient-based transmission map estimation method to estimate the refined transmission map accurately while reducing the computational complexity. The effectiveness of our method is confirmed by extensive experiments over a wide variety of images.

Underwater Image Enhancement Using Adaptive Retinal Mechanisms.

We propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina. We aim to solve the problems of underwater image degradation raised by the blurring and nonuniform color biasing. In particular, the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the nonuniform color bias. The center-surround opponent mechanism of the bipolar cells and the feedback from amacrine cells to interplexiform cells then to horizontal cells serve to enhance the edges and contrasts of the output image. The ganglion cells with color-opponent mechanism are used for color enhancement and color correction. Finally, we adopt a luminance-based fusion strategy to reconstruct the enhanced image from the outputs of ON and OFF pathways of fish retina. Our model utilizes the global statistics (i.e., image contrast) to automatically guide the design of each low-level filter, which realizes the self-adaption of the main parameters. Extensive qualitative and quantitative evaluations on various underwater scenes validate the competitive performance of our technique. Our model also significantly improves the accuracy of transmission map estimation and local feature point matching using the underwater image. Our method is a single image approach that does not require the specialized prior about the underwater condition or scene structure.