Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms

Visibility and distortion measurement for no-reference dehazed image quality assessment via complex contourlet transform

Recently, most dehazed image quality assessment (DQA) methods mainly focus on the estimation of remaining haze, omitting the impact of distortions from the side effect of dehazing algorithms, which lead to their limited performance. Addressing this problem, we proposed a learning both Visibility and Distortion Aware features no-reference (NR) Dehazed image Quality Assessment method (VDA-DQA). Visibility aware features are exploited to characterize clarity optimization after dehazing, including the brightness, contrast, and sharpness aware feature extracted by complex contourlet transform (CCT). Then, distortion aware features are employed to measure the distortion artifacts of images, including the normalized histogram of local binary pattern (LBP) from the reconstructed dehazed image and the statistics of the CCT sub-bands corresponding to chroma and saturation map. Finally, all the above features are mapped into the quality scores by the support vector regression (SVR). Extensive experimental results on six public DQA datasets verify the superiority of proposed VDA-DQA in terms of the consistency with subjective visual perception, and outperforms the state-of-the-art methods.The source code of VDA-DQA is available at https://github.com/li181119/VDA-DQA

Dehazed image quality evaluation: from partial discrepancy to blind perception

Nowadays, vision oriented intelligent vehicle systems such as autonomous driving or transportation assistance can be optimized by enhancing the visual visibility of images acquired in bad weather conditions. The presence of haze in such visual scenes is a critical threat. Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on both synthetic and real dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for image dehazing algorithms and have the potential to be deployed in real transportation systems

Deep learning with multiple modalities : making the most out of available data

L’apprentissage profond, un sous domaine de l’apprentissage machine, est reconnu pour nécessiter une très grande quantité de données pour atteindre des performances satisfaisantes en généralisation. Une autre restriction actuelle des systèmes utilisant l’apprentissage machine en lien avec les données est la nécessité d’avoir accès au même type de données autant durant la phase d’entrainement du modèle que durant la phase de test de celui-ci. Dans plusieurs cas, ceci rend inutilisable en entrainement des données de modalité supplémentaire pouvant possiblement apporter de l’information additionnelle au système et l’améliorer. Dans ce mémoire, plusieurs méthodes d’entrainement permettant de tirer avantage de modalités additionnelles disponibles dans des jeux de données seulement en entrainement et non durant la phase de test seront proposées. Pour débuter, nous nous intéressons à diminuer le bruit présent dans images.. On débute le mémoire avec la technique la plus simple, soit un débruitage avant une tâche pour augmenter la capacité du système à faire cette tâche. Par la suite, deux techniques un peu plus poussées proposant de faire un débruitage guidé pour augmenter les performances d’une tâche subséquente sont présentées. On conclut finalement cette thèse en présentant une technique du nom d’Input Dropout permettant d’utiliser très facilement une modalité seulement disponible en entrainement pour augmenter les performances d’un système, et ce pour une multitude de tâches variées de vision numérique.Deep learning, a sub-domain of machine learning, is known to require a very large amount of data to achieve satisfactory performance in generalization. Another current limitation of these machine learning systems is the need to have access to the same type of data during the training phase of the model as during its testing phase. In many cases, this renders unusable training on additional modality data that could possibly bring additional information to the system and improve it. In this thesis, several training methods will be proposed to take advantage of additional modalities available in datasets only in training and not in testing. We will be particularly interested in reducing the noise present in images. The thesis begins with the simplest technique, which is a denoising before a task to increase the system’s ability to perform a task. Then, two more advanced techniques are presented, which propose guided denoising to increase the performance of a subsequent task. Finally, we conclude this thesis by presenting a technique called Input Dropout that facilitates the use of modality only available in training to increase the performance of a system, and this for a multitude of varied computer vision tasks

A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect