Haze Removal Technique Research Articles

Detection of Illegal Goods using X-ray Image Enhancement Algorithm

bstract: An X-ray image enhancement technique integrating USM+CLAHE+HAZEREMOVAL and YOLOV2 for object detection is presented to address the problem of colour distortion in CLAHE enhanced airport security X-ray images. Calculating the grayscale images on the R, G, and B channels of the X-ray image and applying CLAHE enhancement to each, then merging the enhanced R, G, and B grayscale images will take place. After that, USM sharpening operation is applied to the CLAHE-enhanced X-ray image, and then it is merged with the original and USM-sharpened images according to the weight. Later haze removal technique is added to obtained results. For detection, YOLOV2 is used. The results of the experiments reveal that the USM+CLAHE+ HAZEREMOVAL algorithm can successfully improve the security X-ray image while also suppressing colour distortion in the enhanced image

Weighted Fusion of Pre-processing Techniques for Neural Network-based Image Haze Removal

Haze is the natural phenomenon, which affects an image's air light and visibility. It creates a layer that hides the information in an acquired hazy image and decreases its visibility. Hazy scenarios are mostly seen in the transportation sector and remote sensing. It affects the quality of an image captured. Haze is one of the major hurdles in several computer vision applications. This paper observes and analyses different methods of haze removal via image enhancement techniques. Proposes the weighted average of the image enhancement methods to generate the enhanced hazy input image as the initial step. These enhanced images do train the neural network to estimate transmission map as well as atmospheric light, used for haze removal from images. The proposed method is experimented with 135 hazy images from three standard datasets, alias I-Haze, NH-Haze, and O-Haze (45 images from each total 135 hazy images). It gives clearer results than a few similar existing haze removal techniques. Also, the experimental results tested with performance metrics Entropy PSNR, and SSIM have demonstrated the effectiveness of the proposed haze removal method having weighted fusion of pre-processing techniques.

Compared-neighborhood based image dehazing for improved visibility

Image hazing is the degradation of photographic quality due to light attenuation by mist or suspended particles. This paper presents solutions to two shortcomings of existing haze-removal techniques. One shortcoming is that pixels often get zeroed out in the dehazing process, which suppresses edges and features. The other is that the assumption of homogeneity of features and properties in input images during dehazing reduces the resolution of features and textures. A solution that considers how feature and edge visibility, which are primarily disrupted by noise, is provided. This noise is responsible for the lack of distinction between local and global pixel neighborhoods. An attenuation coefficient that helps to minimize pixel distortion is proposed. This coefficient is sensitive to local relative pixel intensity and prevents the pixels from being zeroed out in the context of certain local or global neighborhoods. The proposed technique is implemented via the existing dual-stream network based on a CNN with the block-greedy algorithm. The qualitative and quantitative evaluation based on 117 images shows 75% improvement in haze density ζ, 90% increase in edge visibility e, and 150% improvement in the peak-signal-to-noise ratio (PNR), and 95% increase in structural similarity index measure (SSIM) compared to the original hazed image. These show a remarkable improvement compared to the existing state-of-the-art methods. The shortcoming is a need for color improvement, which can be studied further in future studies.

3D Visualization of Objects in Heavy Scattering Media by Using Wavelet Peplography

In this paper, we propose three-dimensional(3D) visualization of objects in heavy scattering media by using peplography and wavelet transform. Conventional haze removal techniques can remove the light haze in the image by using various image processing algorithms or machine learning techniques. However, they may not provide a clear image under heavy scattering media. On the other hand, peplography can visualize the object by detecting ballistic object photons from heavy scattering media. Then, 3D image can be generated by integral imaging. However, it may not visualize 3D object information accurately because of the noise photons from scattering media. Therefore, the image quality for 3D object visualization may be degraded. To solve this problem, we use the discrete wavelet transform in peplography. It can detect the object photon signals from the scattering media and enhance 3D image contrast ratio by using a specific coefficient threshold technique. To prove our method, we carry out the optical experiments and compare with conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal to noise ratio.

Single Image Haze Removal Techniques Based on Priors, Fusion and Deep Learning: A Comprehensive Survey

Single Image Haze Removal Techniques Based on Priors, Fusion and Deep Learning: A Comprehensive Survey

A Survey of Recent Advances for Single Image Haze Removal Techniques

A Survey of Recent Advances for Single Image Haze Removal Techniques

Foggy Image Detection and Filtration Methods: Review

Image de-fogging in brightness defined to image calculated in a deprived climate like as fog, rain and ocean and pollutants or dust particles. To alter the fog and some other pollutants from the image, various methods are customized, some mainly utilized methods are DCP, Detection, and Classification of foggy images. Haze is an arrangement of dual components, air-light and DA (Direct Attenuation), low image quality and generates various issues in VS (Video Surveillance), Navigation and Target Tracking, etc. So, its removes from an image, several de-fogging approaches have been discussed in this paper. Image De-fogging can attain utilizing several and single image haze removal techniques. The famous methods are discussed in this paper used for image de-fogging in DCP, Depth-map for accurate estimation, Guided Filter, and Transmission methods. These techniques still efficient in removing haze from images have very high time complexity. The guided filter is a new region preservative filter with region enhancement and smoothing. The previous result was a local linear transformation of the Guided Image. It defines a review of the classification and detection technique of a hazy image. This method mitigates the limitations of filtration and DCP and at the same time preserves the image quality. At that time, described the existing image de-fogging methods containing image restoration, contrast improvement, and fusion-based image de-fogging methods.

A Review of Remote Sensing Image Dehazing.

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

Rain Streaks Removal Using Discrete Cosine Transform and Diffusion based Technique

Images acquired by the camera are sometimes affected by many environmental conditions such as fog, rain, snow, and clouds, etc., which influence the visible quality of the images and several computer vision applications. In this manuscript, we developed a new rain removal technique. Firstly, an exiting haze removal technique is used so that rain streaks could be visible and after that apply Discrete cosine transform (DCT) and diffusion-based technique to remove rain streaks from the rainy images. The experimental section shows the supremacy of the proposed method over the existing methods.

Image Dehazing in Disproportionate Haze Distributions

Haze removal techniques employed to increase the visibility level of an image play an important role in many vision-based systems. Several traditional dark channel prior-based methods have been proposed to remove haze formation and thereby enhance the robustness of these systems. However, when the captured images contain disproportionate haze distributions, these methods usually fail to attain effective restoration in the restored image. Specifically, disproportionate haze distribution in an image means that the background region possesses heavy haze density and the foreground region possesses little haze density. This phenomenon usually occurs in a hazy image with a deep depth of field. In response, a novel hybrid transmission map-based haze removal method that specifically targets this situation is proposed in this work to achieve clear visibility restoration and effective information maintenance. Experimental results via both qualitative and quantitative evaluations demonstrate that the proposed method is capable of performing with higher efficacy when compared with other state-of-the-art methods, in respect to both background regions and foreground regions of restored test images captured in real-world environments.

Single image haze removal using variable fog-weight

In this paper, a new Image Haze Removal technique is proposed using variable fog-weight. The objective of the proposed technique is to improve the visibility, to remove the impact of weather factors and make image more usable. To achieve this, it is required to estimate the transmission map as well as atmospheric light by using Dark Channel Prior. The main contribution in this work, is to vary the fog-weight during transmission estimation. It is varied according to the density of haze in the input image. If the density of the haze in the input image is low, then lower fog-weight value will be applied and if the haze density in the input image is higher, than higher fog-weight value will be applied, according to the haze concentration. A guided filter is used as a refiner of transmission map to avoid the halo artifacts at the boundary of the edge. It provides smoothness to the image and build-up the visibility of the image, so that we restore a superior haze free image. At last, the performance of the proposed method is compared with some of the existing methods in terms of visibility, computational time and avoidance of halo effect.

Color image dehazing using gradient channel prior and guided L0 filter

Images obtained in poor weather circumstances such as fog, haze, smog, and thin cloud, suffer from severe contrast, texture, edge, and color degradation issues. To restore these weather degraded images, haze removal techniques are required. An efficient Gradient channel prior (GCP) is designed in this paper. It overcomes various issues such as texture distortion, transmission map misestimation, color distortion, and edge degradation. Thereafter, the transmission map is further refined using a guided L0 filter. Finally, the restoration model is also improved to reduce the over-saturation of pixels problem associated with the existing haze removal techniques. Extensive experimental results demonstrate that the proposed technique can significantly restore the hazy images, even if images contain high density of haze.

An Edge Optimization based Image Haze Removal Technique using a Single Image

The perceivability and shading loyalty of outside pix are willing to genuine debasement beneath murky or foggy weather. Numerous PC imaginative and prescient applications usesystems which assume that the information is sans dimness. What's greater, while the assets of info are cloudy, the yields of such frameworks could bring about true mistakes. Thusly, picture murkiness expulsion has right down to earth centrality for true programs. In this paper, we advocate an improved environmental dissipating model and present a proficient image murkiness evacuation calculation depending on the stepped forward model. The information photo is first divided into various scenes dependent on the dimness thickness. Next, a scene luminance map is assessed for each scene by using gambling out the averaging and disintegration responsibilities. At that factor, the profundity map is found utilizing a straight model, which makes use of the beauty and immersion segments of the statistics photograph. The transmission map is then assessed by utilising the got depthmap and the dissipating coefficient. Furthermore, a delicate tangling approach is applied for aspect enhancement, to do away with the negative influences triggered from an irrelevant scene department outcomes, and to supply a refined scene luminance guide and transmission map. At last, the improved climatic dispersing model is utilized to accumulate the cloudiness loose yield photograph. The check outcomes exhibit that our technique is a hit in looking after a progression of ordinary troubles like lopsided luminance, over improve and over immersion in pictures. In addition, our strategy outflanks maximum modern-day photo cloudiness expulsion calculations concerning first-rate and protection of edges

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.

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

Haze Removal Using Aggregated Resolution Convolution Network

The haze removal technique refers to the process of reconstructing haze-free images from scenes of inclement weather conditions. This task has an extensive demand in practical applications. At present, models based on deep convolution neural networks have made significant progress in the haze removal field, greatly outperforming the traditional prior and constraint methods. However, the current CNNs methods, which involve only a single input image, do not provide sufficient features to determine the optimal transmission maps for haze removal; therefore, we propose and design an aggregated resolution convolution network (ARCN) that uses multiple inputs and aggregates features from a CNN model and the adversarial loss algorithm. Experiments comparing the visual results of our network with those of several previous methods reveal substantial improvements.

Simulation of Hazy Image and Validation of Haze Removal Technique

Haze hampers the performance of vision systems. So, removal of haze appearance in a scene should be the first-priority for clear vision. It finds wide spectrum of practical applications. A good number of dehazing techniques have already been developed. However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity. To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique. The simulated hazy image is developed using atmospheric model with and without Perlin noise. The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.

Haze removal algorithm based on single-images with chromatic properties

Dense fog conditions formed by particles and water droplets in the atmosphere impact the road segmentation accuracy of Advanced Driver Assistance Systems (ADAS) by degrading the contrast and color fidelity of images. Existing road segmentation methods are not suitable for dense haze conditions; darkness, under-estimation, and over-enhancement problems occur after dehazing. This paper proposes a single-image haze removal technique based on hazy state detection and simple priors. Hazy state detection serves to identify the fog in an image by the linear relationship between brightness and saturation in HSV color space. Simple priors both in the background and hazy layers are based on a dichromatic reflection model (DRM) to accommodate different concentrations of fog. We find that according to color consistency and physical properties, fog and highlight appear to share the same chromatic properties. Based on this observation, the DRM can approximate the fog map. The maximum a posteriori formulation can also be utilized to robustly refine the fog map and remove black noise. We employ the fog map to compute atmospheric light and predict transmission rate in order to obtain haze-free images. Our results show that this method outperforms existing methods in regards to both efficiency and dehazing ability.

Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model

Existing haze removal approaches have unreliable estimation of atmospheric light and transmission, which lead to imprecision color information recovery, gradient reversal artifacts, and halo effect. In this paper, a novel haze removal technique based on ambient light model is presented. Our technique applies canny edge detection algorithm which can locate the sky area accurately, and adopts binary-tree algorithm to obtain the radiance of atmospheric light A rapidly; then, an adaptive wiener filter is employed for modifying transmission map, and halo effect is further restricted in the sudden changed region of scene depth by the morphology method. Experimental results verify that the optimized approach has better robustness, and is superior to present methods.

A Survey on Haze Removal Techniques in Computer Vision and Graphics Application

A Survey on Haze Removal Techniques in Computer Vision and Graphics Application