Deep learning belongs to the family of artificial intelligence and machine learning where the primary objective is to learn and diversify the feature representation for a given system. In deep learning, a machine is able to develop large parameterized models that addresses a plethora of scientific problems based on a number of optimization methods. These models will be capable of retrieving, representing, generating, and combining a large number of features to provide a generalized solution to the intended problems. Unlike traditional machine learning algorithms, deep learning algorithms offer an opportunity to learn, extract, and even generate very large feature spaces via densely parameterized models, which are capable of learning semantic information and an efficient input-output mapping. Hence, they are very suitable in low- level computer vision applications involving multimedia enhancement problems.

Deep learning has a very broad scope, but this thesis is primarily focused on artificial neural networks, convolutional neural networks, and their variants which are some of the most powerful deep learning tools today. In this work, the neural network fundamentals are explained, the corresponding derivations are performed, and the workflows are illustrated. Important modules of convolutional neural networks are described and their functions are discussed. Various convolutional architectures are proposed for various computer vision tasks related to image quality improvement and their suitability towards the particular problems are explained. Various networks, which include novel network modules and architectures, are studied and applied in the areas of image and video enhancement. Ablation studies and experiments are performed on the network architectures to analyze them. Finally, the proposed models are evaluated in terms of their prowess towards the aforementioned vision tasks.