Feature Forwarding for Efficient Single Image Dehazing

Procedia Computer Science, 2019

Haze is a natural phenomenon in which the dust, smoke and other particles alter the vision of the sky to reduce the visibility. Hazy images cause various visibility problems for traffic user, tourists everywhere, especially in hilly areas where haze and fog are very common. In this paper, a method for single image dehazing using convolutional neural network is proposed. Outdoor images have been used on which particular filters are applied to find the haze in image. Hazy images contain small value in only one-color alpha channel from Red, Blue, green RGB channel. The intensity of these pixels is mainly bestowed by air light depth map. Estimating these low value points of haze transmission map are useful to obtain a high quality dehazed image. An end-to-end encoder-decoder training model is utilized to achieve a high quality dehazed image. The approach is validated on datasets which consists of around 1500 outdoor images. The method also gives transmission map of the hazy image which can further be used to enhance visibility of the scene.

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018

This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. 1

IEEE-xplore, 2023

Image dehazing is a rapid research area in the field of video surveillance and computer applications. Images are degraded by the scattering of atmospheric particles in the light, fog, and air particles in the atmosphere. In this paper, we are discussing conventional existing techniques such as the Dark Channel Prior method (DCP), Median Dark Channel Prior Method (MDCP), and Average Image Dehazing Model, but these methods suffer from a lot of computing complexity and even visual distortions like halos and over-saturation. To reduce the computational complexity and recover the original image from the hazy image Deep Learning and Convolutional Neural networks (CNN) are used. In this paper, we discussed various methods to reduce the haze in an image by using a feature/learning-based Convolutional Neural Network (CNN) such as Attention-based Transmission Estimation and Classification Fusion Network (ATECFN), and Encoder-Decoder architecture.

International Journal of Electrical and Computer Engineering (IJECE), 2025

The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-supervised convolutional neural network (CNN). This approach incorporates both the input hazy image and the dark channel prior. By leveraging an encoder, the combined information of the dark channel prior and haze image is encoded into a condensed latent representation. Subsequently, a decoder is employed to reconstruct the clean image using these latent features. Our experimental results demonstrate that our proposed algorithm significantly enhances image quality, as indicated by improved peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. We perform both quantitative and qualitative comparisons with recently published techniques, showcasing the efficacy of our approach.

2019

Most existing dehazing algorithms often use hand-crafted features or Convolutional Neural Networks (CNN)-based methods to generate clear images using pixel-level Mean Square Error (MSE) loss. The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e.g. image classification. In this paper, we investigate a new point of view in addressing this problem. Instead of focusing only on achieving good quantitative performance on pixel-based metrics such as Peak Signal to Noise Ratio (PSNR), we also ensure that the dehazed image itself does not degrade the performance of the high-level vision tasks such as image classification. To this end, we present an unified CNN architecture that includes three parts: a dehazing sub-network (DNet), a classification-driven Conditional Generative Adversarial Networks sub-network (CCGAN) and a classification sub-network (CNet) related to image classification, which has better performance b...

Research Square (Research Square), 2023

Image haze removal has a significant involvement in numerous computer vision (CV) related uses. The primary purpose of the present article is to outline the current deep learning (based on CNN) processes used for single image dehazing. The previous problems with the haze removal methods based on multiple images are first addressed. Then, fundamental concepts of atmospheric scattering model and Convolutional Neural Network (CNN) are explained. The currently available single image dehazing approaches are divided into 3 groups: prior based, image fusion based and deep learning based approaches. Highlights and challenges of these dehazing techniques are discussed. The synthetic and real datasets utilized by different researchers in dehazing techniques are described along with implementation details. The paper also mentions performance metrics for evaluating image quality.

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were ∼ 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

Computer Vision – ECCV 2020 Workshops, 2020

Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

Haze in images is due to natural environmental phenomena, which makes the image in a white shade noise. Haze removal is one of the most important research topics these days to due popularity of applications in real time surveillance from drones or any area under security. Both indoor and outdoor images are important for testing haze and its removal. Many image processing techniques are made by researchers to remove haze in a single image. Haze intensity can be calculated by a parameter known as perceptual fog density (PFD). It is important to analyze this parameter for all the techniques so as to get an idea of improvement. In this paper , a new approach is made by applying globally guided filtering technique with deep neural network. This proposed algorithm is implemented on MATLAB software and results are obtained by calculating the PFD in the existing and proposed technique. The four techniques are compared with each other. The techniques are global filtering (GIF), weighted global filtering(WGIF), Globally guided filtering(GGIF) and proposed technique i.e. Globally guided filtering with DNN (Deep Neural Network). In GIF, the fine structure of the image is generally not preserved and unrealistic image is obtained. In WGIF, the PFD obtained is highest. In GGIF, PFD is lower and Structure is not preserved, but in proposed algorithm, the PDF is minimum with fine structure, color intensity of the picture is of the best quality.

2018 Colour and Visual Computing Symposium (CVCS), 2018

We compare a recent dehazing method based on deep learning, Dehazenet, with traditional state-of-the-art approaches, on benchmark data with reference. Dehazenet estimates the depth map from transmission factor on a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that the transmission is very well estimated by the network, but also that this method exhibits the same limitation than others due to the use of the same imaging model.