Super-resolution from image sequences-a review

By Sean Borman, R. Stevenson. 1998

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, background): “Here we will focus on single image super-resolution (SISR) and will not further discuss approaches that recover HR images from multiple images, such as object images acquired from varying view points or temporal sequences of image frames [4, 11].”
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016, neutral): “These can be categorised as multi-image SR methods [1, 11] and exploit explicit redundancy by constraining the ill-posed problem with additional information and attempting to invert the downsampling process.”
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network (2018, background): “Though there exist extensive literature studies on multi-image SR [6,38,14], here we limit our discussions to SISR works alone.”
Super-resolution: a comprehensive survey (2014, methodology): “Several survey papers [47], [48], [131], [132], [155], [198], [265], [292], [350], [416] have also been published on the topic.”
Advances and challenges in super‐resolution (2004, background): “Although several articles have surveyed the different classical Super-Resolution methods and compared their performances (e.g., Borman and Stevenson, 1998; Kang and Chaudhuri, 2003), the intention of this article is to pinpoint the various difficulties inherent to the Super-Resolution problem for a…”
Multiframe demosaicing and super-resolution of color images (2006, background): “Surpassing this limit can be achieved by acquiring and fusing several low-resolution (LR) images of the same scene, producing high-resolution (HR) images; this is the basic idea behind super-resolution techniques [1]–[4].”
Super-resolution image reconstruction: a technical overview (2003, methodology): “The technical report by Borman and Stevenson [2] provides a comprehensive and complete overview on the SR image reconstruction algorithms until around 1998, and a brief overview of the SR techniques appears in [3] and [4].”
A survey on super-resolution imaging (2011, background): “Super-resolution (SR) imaging [1–20] aims to overcome or compensate the limitation or shortcomings of the image acquisition device/system and/or possibly ill-posed acquisition conditions to produce a higher-resolution image based”
Fast and Robust Multi-Frame Super-Resolution (2004, background): “Although the frequency domain methods are intuitively simple and computationally cheap, they are extremely sensitive to model errors [3], limiting their use.”
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution (2012, background): “This approach relates to a large bulk of image processing literature [14], [15], [16], [17], [18], [19].”
Fast and robust multiframe super resolution (2004, background): “Although the frequency domain methods are intuitively simple and computationally cheap, they are extremely sensitive to model errors [ 3 ], limiting their use.”
Super-Resolution for Remote Sensing Images via Local–Global Combined Network (2017, background): “There have been many earlier researches on image superresolution, most of which are designed for multiple images, where a series of low-resolution images (different acquisition time of the same scene) are used to recover the high-resolution image [2].”