Explore the vast range of titles available.

The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays. The proposed work presents a fast and accurate method for single image dehazing. The proposed method works in two folds; (i) An adaptive dehazing control factor is proposed to estimate accurate transmission, which is based on difference of maximum and minimum color channel of hazy image, and (ii) a mathematical model to compute probability of a pixel to be at short distance is presented, which is utilized to locate haziest region of the image to compute the value of atmospheric light. The proposed method obtains visually compelling results, and recovers the information content (such as structural similarity, color, and visibility) accurately. The computation speed and accuracy of the proposed method is proved using quantitative and qualitative comparison of results with state of the art dehazing methods.

Single image dehazing (SID) solves the atmospheric scattering model (ATSM). The ill-defined nature of the SID makes it a challenging problem. The transmission is the prime parameter of ATSM. Hence, accurate transmission is essential for quality of SID. The existing methods of SID estimate the transmission based on priors with strong assumptions (such as dark channel prior). These methods do not recover original colors, structure and visibility due to wrong transmission under invalidity of these assumptions. Therefor, the difference channel (DCH) is proposed to estimate accurate transmission. The DCH non-linearly translates the minimum channel of hazy image into minimum channel of haze-free image, which is used to compute the value of transmission. The DCH is based on an observation that difference of maximum and minimum color channel of the hazy image is negatively correlated with depth. The proposed method is able to recover the details from hazy image in the form of structure, edges, corners, colors and visibility due to the DCH. The accuracy and robustness of the proposed method is proved by comparing the results with known dehazing methods based on qualitative and quantitative analysis using benchmark data sets.

There exist multiple dehazed images corresponding to a single hazy image due to ill-posed nature of single image dehazing (SID), making it a challenging problem. Usually, the SID used atmospheric scattering model (ASM) to obtain haze-free image from a hazy image. According to ASM, recovery of lost visibility depends upon accurate transmission. The proposed method presents a linear multiplicative bounding function (MBF) for estimation of difference channel (DC) to compute the value of transmission. The results obtained by the MBF has been compared with renowned SID methods. The accuracy of the proposed MBF has been proved by visual and objective evaluation of the dehazed images.

Effective functioning of outdoor vision systems depends upon the quality of input. Varying effects of light create different weather conditions (like raining, snowfall, haze, mist, fog, and cloud) due to optical properties of light and physical existence of different size particles in the atmosphere. Thus, outdoor images and videos captured in adverse environmental conditions have poor visibility due to scattering of light by atmospheric particles. Visibility restoration (dehazing) of degraded (hazy) images is critical for the useful performance of outdoor vision systems. Most of the existing methods of image dehazing considered atmospheric scattering model (ASM) to improve the visibility of hazy images or videos. According to ASM, the visual quality of dehazed image depends upon accurate estimation of transmission. Existing methods presented different priors with strong assumptions to estimate transmission. The proposed method introduces a tight lower bound on transmission. However, the accuracy of the proposed tight lower bound depends upon minimum color channel of haze-free image. Therefore, a prior is proposed to estimate the minimum color channel of the haze-free image. Furthermore, a blind assessment metric is proposed to evaluate the dehazing methods. Restored and matching corner points of the hazy and haze-free image are used to compute the proposed blind assessment metric. Obtained results are compared with renowned dehazing methods by qualitative and quantitative analysis to prove the efficacy of the proposed method.

Deep Learning models are widely used to address COVID-19 challenges, but they require a large number of training samples. X-ray images of COVID-19 patients are amongst the preferred methods for detection. However, their availability is limited. In contrast, X-ray images of non-COVID-19 patients are available in abundance. Furthermore, COVID-19 patient's treatment varies based on infection severity. This leads to a class imbalance issue as there are far more X-ray images of non-COVID patients than COVID-19 patients available for training deep learning models. As a result, deep learning models cannot achieve the desired levels of accuracy. This study's primary objective is to generate synthetic X-ray images depicting three levels of severity, utilizing a Cycle Consistent Generative Adversarial Network (CycleGAN). The Structural Similarity Index (SSIM) is employed to create training datasets for three severity levels, which are then used to train the corresponding CycleGAN models. Additionally, a comparative analysis is conducted to compare the achieved accuracies between X-ray images of COVID-19 patients and non-COVID-19 patients. This analysis involves datasets containing authentic non-synthetic COVID-19 X-ray images and datasets containing synthetic COVID-19 X-ray images generated using CycleGAN. The results indicate enhanced accuracy when deep models are trained using augmented X-ray data. This study is novel as no prior work has been done on severity-wise dataset generation and classification of COVID-19 X-ray images.

Outdoor images lose color contrast and visibility in poor weather conditions (like fog, mist, haze and rain), which affects computer vision applications extremely. Degree of degradation at a pixel varies with the depth of a scene point from the observer. Therefore, the problem of image restoration under bad weather is expressed as depth estimation of each scene point from degraded image. The proposed work introduces a linear depth model based on color attenuation prior to estimate depth of each scene point from a single image. The proposed work is based on an observation that the difference of saturation from brightness and hue increases with scene depth and preserves structural similarity of degraded image. The proposed work is capable to preserve the existing edges and recover both the scene depth and the degraded edges. Effectiveness and accuracy of the proposed method is measured qualitatively and quantitatively. The experimental result analysis proves that the proposed method outruns in comparison to the live state of art methods.

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.