A two-step neural-network based algorithm for fast image super-resolution

Miravet, Carlos; Rodríguez, Francisco B.

doi:10.1016/j.imavis.2006.12.016

Cited by 26 publications

(15 citation statements)

References 53 publications

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“…This algorithm was later on used (or slightly changed) in many other works [13], [14], [24], [51], [91], [172], [196], [264] [284], [303], [304], [306], [307], [319], [480], [481], [482], [537], [574], [575].…”

Section: Geometric Registrationmentioning

confidence: 99%

“…Examples of such networks are Linear Associative Memories (LAM) with single [61] and dual associative learning [192], Hopfield NN [96], [326], Probabilistic NN [130], [304], Integrated Recurrent NN [136], Multi Layer Perceptron (MLP) [196], [354], [385], [547], Feed Forward NN, [232], [233], and Radius Basis Function (RBF) [327], [607].…”

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…-some of the learning based approaches perform fast in their testing phase, but they need a time consuming training step and are mostly application dependent, like in [130], [292], [304].…”

Section: Databasesmentioning

confidence: 99%

“…The latter solution increases the capacitance of the system, which slows down the charge transfer rate. Furthermore, the mentioned hardware-based Satellite and Aerial imaging [3], [5], [6], [8], [9], [10], [22], [25], [28], [41], [51], [52], [54], [55], [59], [70], [79], [96], [104], [107], [108], [109], [113], [114], [157], [159], [160], [161], [167], [175], [196], [203], [234], [247], [258], [290], [304], [306], [345], [352], [371], [598], [603], [606], [613] Medical Image Processing [15], [27], [95], [107], [108], …”

Section: Introductionmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Section: Geometric Registrationmentioning

confidence: 99%

Section: Learning Based Single Image Sr Algorithmsmentioning

confidence: 99%

“…-some of the learning based approaches perform fast in their testing phase, but they need a time consuming training step and are mostly application dependent, like in [130], [292], [304].…”

Section: Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

View full text Add to dashboard Cite

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“…38 The algorithm is based on the application of scattered-point interpolation on projected sequence data, followed by a filtering operation to restore degradations associated to sequence pixel size and residual errors introduced by interpolation. The scattered-point interpolation module has been implemented using a novel hybrid neural network architecture, enabling the processing of synthetic sequences to learn optimum distance-based interpolation functions for different noise levels in the input sequence.…”

Section: Introductionmentioning

confidence: 99%

Superresolution imaging: a survey of current techniques

et al. 2008

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Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

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