A multi-frame image super-resolution method

By Xuelong Li, Yanting Hu, Xinbo Gao, Dacheng Tao, Beijia Ning. Signal Processing, 2010

297 citations recorded by the source corpus.

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/62f7a4195981cae0685a874d28480d1b2c0a0f60

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, background): “Spatial domain methods attempt to utilize the correlations, which exist in most natural images [4].”
Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model (2012, background): “In addition, some spatially adaptive TV (SATV) models have been proposed for gray-level image denoising to better deal with the noise suppression and edge preserving problem [29]– [32].”
Super-resolution: a comprehensive survey (2014, methodology): “In [472] a locally adaptive version of BTV, LABTV, has been introduced to provide a balance between the suppression of noise and the preservation of image details [472].”
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression (2012, background): “Representative priors are smoothness prior [8], [9], edge smoothness prior [35], [36], and co-occurrence prior [13]–[15].”