Removal of visual disruption caused by rain using cycle-consistent generative adversarial networks

This paper addresses the problem of removing rain disruption from images without blurring scene content, thereby retaining the visual quality of the image. This is particularly important in maintaining the performance of outdoor vision systems, which deteriorates with increasing rain disruption or degradation on the visual quality of the image. In this paper, the Cycle-Consistent Generative Adversarial Network (CycleGAN) is proposed as a more promising rain removal algorithm, as compared to the state-of-the-art Image De-raining Conditional Generative Adversarial Network (ID-CGAN). One of the main advantages of the CycleGAN is its ability to learn the underlying relationship between the rain and rain-free domain without the need of paired domain examples, which is essential for rain removal as it is not possible to obtain the rain-free image under dynamic outdoor conditions. Based on the physical properties and the various types of rain phenomena [10], five broad categories of real rain distortions are proposed, which can be applied to the majority of outdoor rain conditions. For a fair comparison, both the ID-CGAN and CycleGAN were trained on the same set of 700 synthesized rain-and-ground-truth image-pairs. Subsequently, both networks were tested on real rain images, which fall broadly under these five categories. A comparison of the performance between the CycleGAN and the ID-CGAN demonstrated that the CycleGAN is superior in removing real rain distortions

Networks are Slacking Off: Understanding Generalization Problem in Image Deraining

Deep deraining networks, while successful in laboratory benchmarks, consistently encounter substantial generalization issues when deployed in real-world applications. A prevailing perspective in deep learning encourages the use of highly complex training data, with the expectation that a richer image content knowledge will facilitate overcoming the generalization problem. However, through comprehensive and systematic experimentation, we discovered that this strategy does not enhance the generalization capability of these networks. On the contrary, it exacerbates the tendency of networks to overfit to specific degradations. Our experiments reveal that better generalization in a deraining network can be achieved by simplifying the complexity of the training data. This is due to the networks are slacking off during training, that is, learning the least complex elements in the image content and degradation to minimize training loss. When the complexity of the background image is less than that of the rain streaks, the network will prioritize the reconstruction of the background, thereby avoiding overfitting to the rain patterns and resulting in improved generalization performance. Our research not only offers a valuable perspective and methodology for better understanding the generalization problem in low-level vision tasks, but also displays promising practical potential

Time series prediction and forecasting using Deep learning Architectures

Nature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures

Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing

Hazy images obscure content visibility and hinder several subsequent computer vision tasks. For dehazing in a wide variety of hazy conditions, an end-to-end deep network jointly estimating the dehazed image along with suitable transmission map and atmospheric light for guidance could prove effective. To this end, we propose an Iterative Prior Updated Dehazing Network (IPUDN) based on a novel iterative update framework. We present a novel convolutional architecture to estimate channel-wise atmospheric light, which along with an estimated transmission map are used as priors for the dehazing network. Use of channel-wise atmospheric light allows our network to handle color casts in hazy images. In our IPUDN, the transmission map and atmospheric light estimates are updated iteratively using corresponding novel updater networks. The iterative mechanism is leveraged to gradually modify the estimates toward those appropriately representing the hazy condition. These updates occur jointly with the iterative estimation of the dehazed image using a convolutional neural network with LSTM driven recurrence, which introduces inter-iteration dependencies. Our approach is qualitatively and quantitatively found effective for synthetic and real-world hazy images depicting varied hazy conditions, and it outperforms the state-of-the-art. Thorough analyses of IPUDN through additional experiments and detailed ablation studies are also presented.Comment: First two authors contributed equally. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Project Website: https://aupendu.github.io/iterative-dehaz