A Framework For Single Image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

The quality of images captured in bad weather is often affected by chromatic casts and low visibility due to the presence of atmospheric particles. Restoration of the color balance is often ignored in most of the existing image de-hazing methods. In this paper, we propose a varicolored end-to-end image de-hazing network which restores the color balance in a given varicolored hazy image and recovers the haze-free image. The proposed network comprises of 1) Haze color correction (HCC) module and 2) Visibility improvement (VI) module. The proposed HCC module provides required attention to each color channel and generates a color balanced hazy image. While the proposed VI module processes the color balanced hazy image through novel inception attention block to recover the haze-free image. We also propose a novel approach to generate a large-scale varicolored synthetic hazy image database. An ablation study has been carried out to demonstrate the effect of different factors on the performance of the proposed network for image de-hazing. Three benchmark synthetic datasets have been used for quantitative analysis of the proposed network. Visual results on a set of real-world hazy images captured in different weather conditions demonstrate the effectiveness of the proposed approach for varicolored image de-hazing.

Images captured in poor lighting conditions (such haze, fog, mist, or smog) have a lower level of visibility because air particles deflect light. Single picture dehazing techniques can restore clarity to a single hazy image. Even though Dark channel prior (DCP) has been the most used method for image dehazing algorithms, it has poor picture quality and requires a lot of adjustments for real time applications such as; computer vision, object detection. However, previous studies such as; Dark Channel Prior (DCP) on image dehazing agonises from a gigantic processing time and sleaze of the original image. In addition, the dimensions of the images have a significant effect on the performance of the dehazing algorithms. Hence, the main motive of this work is to minimize the computational overload either by reducing the size of the image or by accurate transmission map estimation without compromising the image quality. Hence, this work is focused on reducing the computational time for image dehazing through multilevel discrete (Haar) wavelet transform, in which the image dimensions has been reduced without degrading quality. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 90% when compared to DCP without degrading image quality.

Single image dehazing is a challenging while important problem as the existence of haze hinders most high-level computer vision tasks. Previous methods solve this problem using various hand-crafted priors or by CNN learning on synthetic data sets. In practice, many CNN based methods estimate the transmission maps and atmospheric lights without considering the pre-defined priors, and always need huge data to train the model. In this work, we propose Dark Channel Prior Guided Conditional Generative Adversarial Network, an end-to-end model that generates realistic haze-free images using hazy image input and dehaze image based on dark channel prior. A Siamese like encoder is proposed to extracted the feature, and multi-scale features are enhanced by feature aggregation block for decoding. Our algorithm can efficiently combine the prior-based and CNN based image dehazing method. Experimental results on synthetic datasets and real-world images demonstrate our model can generate more perceptually appealing dehazing results, and provide superior performance compared with the state-of-the-art methods.

Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. In order to address the above-mentioned problems caused by traditional image dehazing methods, an improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the PSNR and entropy with the proposed method increased by 21.81% and 5.71%, and MSE decreased by 40.07% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95% and entropy by 2.04% and a decrease of MSE by 84.78%. The results from this study can provide a reference for agricultural field monitoring. Keywords: agricultural monitoring, image dehazing, monitoring image, dark channel prior (DCP), brightness promoting DOI: 10.25165/j.ijabe.20181102.3357 Citation: Wang X Y, Yang C H, Zhang J, Song H B. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. Int J Agric & Biol Eng, 2018; 11(2): 170–176.

Due to the environmental factor like unbalanced air light, darkness, contrast, saturation and attenuation causes hazy image. Many techniques are used to produce good quality image that retain color, illumination and better edges which suffer from weak edges and halo artifacts. In this paper we will be discussing previously proposed method which is adopted to remove haze from image. Several methods have been adopted to remove haze such as image dehazing is classified into two types such as single image dehazing and multi image dehazing. Different techniques of Image dehazing have been discussed in this review and also their advantage and drawback.

The paper proposes bright and dark channel priors dependent single image based dehazing in two color spaces. The hazy RGB is first converted into YC b C r space. Transmission maps are estimated using dark channel priors (DCPs) and airlight of intensity (in YC b C r ) and RGB components computed using three window sizes. Bright channel priors (BCPs) and threshold values are also computed for YC b C r and RGB color spaces. The transmission maps are adjusted using DCPs and BCPs and refined using multiscale guided filter to obtain a superior dehazed image. Comparison (both visually and qualitatively) on variety of images exhibits the significance of proposed technique over existing techniques.

Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in hazy weather. In this chapter, we first propose a single-image dehazing method. We estimate the initial transmission properly based on latent region segmentation and refine the estimated initial transmission by an objective function with a novel weighted L1-norm regularization term. The half-quadratic splitting minimization method is employed to solve this optimization problem. We also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light, we recover the haze-free image by the haze imaging model. We then propose a retinex-based decomposition model for a hazy image and a novel end-to-end image dehazing network. In the model, the illumination of the hazy image is decomposed into natural illumination for the haze-free image and residual illumination caused by haze. Based on this model, we design a deep retinex dehazing network (RDN) to jointly estimate the residual illumination map and the haze-free image. Our RDN consists of a multiscale residual dense network and a U-Net with channel and spatial attention mechanisms. The multiscale residual dense network can simultaneously capture global contextual information from small-scale receptive fields and local detailed information from large-scale receptive fields to precisely estimate the residual illumination map caused by haze. And in the dehazing U-Net, we apply the channel and spatial attention mechanisms in the skip connection to achieve a trade-off between overdehazing and underdehazing by automatically adjusting the channel-wise and pixel-wise attention weights so as to obtain a refined haze-free image.

Poor weather circumstances including fog, dust, and atmospheric dispersion of other particles can degrade the clarity of photos taken in outdoor settings. High-level computer vision tasks like picture segmentation and object detection may become more difficult as a result of this issue. However, earlier research on image dehazing, such as Dark Channel Prior (DCP), suffers from a significant computing burden and degradation of the original image, such as oversaturation and halos. In this work, an improved image dehazing approach (Fast DCP) based on down sampling has been proposed. The proposed algorithm executes the dehazing of a haze image with a smaller amount computational time without degrading the quality of the image. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 70% when compared to DCP without degrading image quality.

Image dehazing is an ill-posed problem that has been extensively studied in the recent years. The objective performance evaluation of the dehazing methods is one of the major obstacles due to the lacking of a reference dataset. While the synthetic datasets have shown important limitations, the few realistic datasets introduced recently assume homogeneous haze over the entire scene. Since in many real cases haze is not uniformly distributed we introduce NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. This is the first nonhomogeneous image dehazing dataset and contains 55 outdoor scenes. The non-homogeneous haze has been introduced in the scene using a professional haze generator that imitates the real conditions of hazy scenes. Additionally, this work presents an objective assessment of several state- of-the-art single image dehazing methods that were evaluated using NH-HAZE dataset.

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called "Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. Phone-Hazy and code will be available at https://fanjunkai1.github.io/projectpage/NSDNet/index.html.

Most existing image dehazing methods deteriorate to different extents when processing hazy inputs with noise. The main reason is that the commonly adopted two-step strategy tends to amplify noise in the inverse operation of division by the transmission. To address this problem, we learn an interleaved Cascade of Shrinkage Fields (CSF) to reduce noise in jointly recovering the transmission map and the scene radiance from a single hazy image. Specifically, an auxiliary shrinkage field (SF) model is integrated into each cascade of the proposed scheme to reduce undesirable artifacts during the transmission estimation. Different from conventional CSF, our learned SF models have special visual patterns, which facilitate the specific task of noise reduction in haze removal. Furthermore, a numerical algorithm is proposed to efficiently update the scene radiance and the transmission map in each cascade. Extensive experiments on synthetic and real-world data demonstrate that the proposed algorithm performs favorably against state-of-the-art dehazing methods on hazy and noisy images.

DADRnet: Cross-domain image dehazing via domain adaptation and disentangled representation

Dehazing from a single hazy image has been a crucial task for both computer vision and computational photography applications. While various methods have been proposed in the past decades, estimation of the transmission map remains a challenging problem due to its ill-posed nature. Moreover, because of the inevitable noise generated during imaging process, visual artifacts could be extremely amplified in the recovered scene radiance in densely hazy regions. In this paper, a novel variational regularized single image dehazing (VRD) approach is proposed to accurately estimate the transmission map and suppress artifacts in the recovered haze-free image. Firstly, an initial transmission is coarsely estimated based on a modified haze-line model. After that, in order to preserve the local smoothness property and depth discontinuities in the transmission map, a novel non-local Total Generalized Variation regularization is introduced to refine the initial transmission. Finally, a transmission weighted non-local regularized optimization is proposed to recover a noise suppressed and texture preserved scene radiance. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate that the proposed VRD method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.

Haze is a common phenomenon often produced during bad weather that obscure scenes, reduces visibility, and degrades colors. Haze removal poses a serious challenge because of the difficulty to develop an exact mathematical model when a single hazy image is given as the input. In this paper, we propose a single image haze removal method to highlight the edges. A edge-aware constraint weighting scheme based on first-order derivative from Gradient domain Guided Image filter is adopted in this algorithm which helps to preserve the edges better. In this dehazing algorithm, simplified dark channel of the haze image can be separated as base layer and detail layer by applying a filter, i.e., weighted guided image filter. The transmission map is obtained from the base layer and thereby it is utilized to restore haze-free image. Result analysis show that the proposed algorithm preserves and highlights the edges without any color distortion. Experimental results are performed on natural haze images, aerial images, and under water images. In qualitative analysis, edge map shows the better performance of the proposed method. Quantitative Analysis have been carried out which demonstrates the better performance of the proposed algorithm when compared with the existing method.

Data-driven methods have demonstrated great potential in single image dehazing, most of which are trained using the hazy images synthesized by the atmospheric scattering model. However, the model cannot accurately describe the complicated degradation caused by haze. At the same time, it is impractical to obtain the pixel-to-pixel aligned clear-scene counterparts of real-world hazy images for supervised training. In this letter, we formulate dehazing as a semi-supervised domain translation problem. For better generalization, two auxiliary domain translation tasks are designed to capture the properties of real-world haze and align synthetic hazy images to real-world ones to reduce the domain gap. Dehazing and the auxiliary tasks are conducted in shared latent spaces by a unified framework, and we use differential optimization to search the architectures of the framework. We evaluate the efficacy of the proposed work using one synthetic and three real-world benchmarks that cover the challenging cases in wild scenarios, and it outperforms state-of-the-art algorithms on these benchmarks. The benefits brought by auxiliary domain translation tasks and architecture search are also verified by ablation experiments.