Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

Removing Atmospheric Noise Using Channel Selective Processing For Visual Correction

In the presented paper; we propose an effective image fog removal technique with a color stabilization technique which is a total 2-level process for image restoration with a HSI (Hue Saturation Intensity) based evaluation process. The approach uses extraction of suppressed pixels from an RGB image affected by smoke, steam, fog which is form of white and Gaussian noise. From our observation of most images in fog environment contain some pixels which have low values of luminescence in every color channel (considering RGB image).Using this model, we can directly estimate the effective density of fog and recover the most affected parts in the image. The parameter of calculating the effective luminescence which is a form of intensity, and also gives the scattering estimates of the light, the combined Laplace of the luminescence-light and suppressed pixels values gives us the basic map of light spread which is further used in the restoration of intensity. The transmission of intensity between the calculated fog values in the image give the estimate for the local transition between the intensity values and color values. This factor helps in the color restoration of the affected image and estimates the proper restoration of image after removal of dense fog particles. After the removal of fog particles, we then restore the color balance in the image using an auto-color-contrast stabilization technique. This is the 2-level fog restoration method. The visibility is highly dependent on the saturation of color values and not over saturation, which accounts for image quality improvements. In order to evaluate in-depth the effectiveness, we have also introduced the HSI mapping of the images, as this will show the true restoration of intensity and saturation in the fog image. Results on various images demonstrate the power of the proposed algorithm. To measure the efficiency of the algorithm the parameter of visual index is also estimated which further evaluates the robustness of the proposed algorithm for the HVS (Human Visual System) for the de-fogged images

Haze visibility enhancement: A Survey and quantitative benchmarking

This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are categorized into: multiple image methods, polarizing filter-based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well-known single-image methods, based on a recent dataset provided by Fattal (2014) and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.This study is supported by an Nvidia GPU Grant and a Canadian NSERC Discovery grant. R. T. Tan’s work in this research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre in Singapore Funding Initiativ

Haze Removal in Color Images Using Hybrid Dark Channel Prior and Bilateral Filter

Haze formation is the combination of airlight and attenuation. Attenuation decreases the contrast and airlight increases the whiteness in the scene. Atmospheric conditions created by floting particles such as fog and haze, severely degrade image quality. Removing haze from a single image of a weather-degraded scene found to be a difficult task because the haze is dependent on the unknown depth information. Haze removal algorithms become more beneficial for many vision applications. It is found that most of the existing researchers have neglected many issues; i.e. no technique is accurate for different kind of circumstances. The existing methods have neglected many issues like noise reduction and uneven illumination which will be presented in the output image of the existing haze removal algorithms. This dissertation has proposed a new haze removal technique HDCP which will integrate dark channel prior with CLAHE to remove the haze from color images and bilateral filter is used to reduce noise from images. Poor visibility not only degrades the perceptual image quality but it also affects the performance of computer vision algorithms such as surveillance system, object detection, tracking and segmentation. The proposed algorithm is designed and implemented in MATLAB. The comparison between dark channel prior and the proposed algorithm is also drawn based upon some standard parameters. The comparison has shown that the proposed algorithm has shown quite effective results