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.
Highlights
This review article is based on super-high-definition image generation methods and introduces deep neural networks (DNN) that convert noisy low-resolution images into clean high-resolution images
DNN-based super-resolution techniques tend to maintain the robustness of the super-resolution performance in any direction of the input low-resolution patch by utilizing a public training dataset comprising image patches with various directions [6,7,8,9]
Among several image denoising areas such as reductions of Gaussian noises [12,13,14,15], Poisson noises [3,5,16,17], Poisso-Gaussian noises [18,19,20,21], impulse noises [22,23,24,25], compressed noises [26,27,28,29,30,31], and compressed Poisson noises, this section introduces compressed Poisson noise reduction that the researchers may not find familiar, but it is very important for achieving super-high-definition imaging
Summary
This review article is based on super-high-definition image generation methods and introduces deep neural networks (DNN) that convert noisy low-resolution images into clean high-resolution images. For a certain noise image quality both thethe flatimages and edge regions low computational complexity—the bilateral this limitation, filters have devised improve fortofilter both the flat and method calculates the weighted average of neighboring pixel values usingquality aproposed. Gaussian filter isseveral the representative filter been [2] This method flattens the edge regions while generating clear high-quality images by the well-known ringing or blocking artifacts has a limitation in removing compressed. Generated the bilateral filter that is mainly proposed to remove the well-known ringing or blocking image quality for both the flatimage and edge regions with computational complexity—the bilateral reconstruct a high-resolution by removing thelow compressed. A method tohas more accurately reconstruct a high-resolution image the well-known ringing or blocking artifacts a limitation in techniques removing compressed.
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