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The paper explores literature on the video super resolution by neural network, with all essential basics related to it. The extensive applicability and need of enhanced resolution becomes attraction for researchers. The limitations of traditional methods gives rise to the new generation of neural network based super resolution. The neural networks are well known for parallel and fast computation of data. But embedding a neural network with challenging super resolution era has come up with benefits as well as drawbacks. Still the researchers are working on the challenges like, limited practical feasibility and utility, accuracy at the time cost, complexity, etc. This paper is useful for new researchers to get information about the basics of super resolution and neural network, relative study of learning processes, comparative summarization of neural network architecture used for resolution improvement.

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this paper describes face detection method using skin color information. Processing color information is faster than processing other facial features. There are different color models present for skin color segmentation like RGB, CMY, YUv, YIQ, YCbCr, YCgCr, HSI, etc. But to choose best option among these models for efficient skin color segmentation is difficUlt task. In this paper we have explored and analyse three most popular color models-YCbCr, YCgCr and HSI for face detection.

The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.