Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Single image defogging via multi-exposure image fusion and detail enhancement

Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.

Multiscale Image Dehazing and Restoration:An Application for Visual Surveillance

The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.

A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory

Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal performances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhancement and image restoration for defogging were introduced. The study proposed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehazing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.

Determination of Arctic melt pond fraction and sea ice roughness from Unmanned Aerial Vehicle (UAV) imagery

Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the 7th Chinese National Arctic Research Expedition, aerial photographs were taken from an Unmanned Aerial Vehicle over an ice floe in the Canada Basin. Using threshold discrimination and three-dimensional modeling, we estimated a melt pond fraction of 1.63% and a regionally averaged surface roughness of 0.12 for the study area. In view- of the particularly foggy environment of the Arctic, aerial images were defogged using an improved dark channel prior based image defog algorithm, especially adapted for the special conditions of sea ice images. An aerial photo mosaic was generated, melt ponds were identified from the mosaic image and melt pond fractions were calculated. Three-dimensional modeling techniques were used to generate a digital elevation model allowing relative elevation and roughness of the sea ice surface to be estimated. Analysis of the relationship between the distributions of melt ponds and sea ice surface roughness show-s that melt ponds are smaller on sea ice with higher surface roughness, while broader melt ponds usually occur in areas where sea ice surface roughness is lower.

Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey

In underwater scenes,the quality of the video and image acquired by the underwater imaging system suffers from severe degradation,influencing target detection and recognition.Thus,restoring real scenes from blurred videos and images is of great significance.Owing to the light absorption and scattering by suspended particles,the images acquired often have poor visibility,including color shift,low contrast,noise,and blurring issues.This paper aims to classify and compare some of the significant technologies in underwater image defogging,presenting a comprehensive picture of the current research landscape for researchers.First we analyze the reasons for degradation of underwater images and the underwater optical imaging model.Then we classify the underwater image defogging technologies into three categories,including image restoration approaches,image enhancement approaches,and deep learning approaches.Afterward,we present the objective evaluation metrics and analyze the state-of-the-art approaches.Finally,we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.

Curvelet transform and contrast adaptive clip histogram equalization-based image defogging algorithm

Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast limited adaptive histogram equalization （CLAHE） algorithm, a curvelet transform and contrast adaptive clip histogram equalization （HE）-based foggy day image enhancement algorithm is proposed. The proposed algorithm transforms an image to the curvelet domain and enhances the image detail information via a nonlinear transformation of high frequency curvelet coefficients. After curvelet reconstruction, the contrast adaptive clip HE method is adopted to enhance the total image contrast and the foggy day image contrast and detail information. During the histogram clipping process, the clip limit value is adaptively selected based on image contrast and the sub-block image histogram variance. A comparative analysis of the foggy day image enhancement results are obtained by applying CLAHE, and some classical single image defogging algorithms and the proposed algorithm are also conducted to prove the effectiveness of the proposed algorithm with objective parameters.

Surveillance Video Defogging Algorithm Optimized by Background Extraction

To reduce the flicker artifacts caused by video defogging,a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed.First,an inter frame consistency constraint is constructed and applied to background modeling.Second,the extracted background is defogged with an improved static defogging approach.Third,the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and background.Experimental results show that our algorithm can remove fog effectively and preserve the temporal coherence well.