Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Abstract

Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.