• Publications
  • Influence
Fast and robust multiframe super resolution
Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed.Expand
  • 1,785
  • 158
Kernel Regression for Image Processing and Reconstruction
  • H. Takeda, Sina Farsiu, P. Milanfar
  • Mathematics, Computer Science
  • IEEE Transactions on Image Processing
  • 1 February 2007
In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. InExpand
  • 1,209
  • 132
Static and space-time visual saliency detection by self-resemblance.
We present a novel unified framework for both static and space-time saliency detection. Our method is a bottom-up approach and computes so-called local regression kernels (i.e., local descriptors)Expand
  • 609
  • 85
Analysis versus synthesis in signal priors
The concept of prior probability for signals plays a key role in the successful solution of many inverse problems. Much of the literature on this topic can be divided between analysis-based andExpand
  • 625
  • 54
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
Super-resolution reconstruction proposes a fusion of several low-quality images into one higher quality result with better optical resolution. Classic super-resolution techniques strongly rely on theExpand
  • 647
  • 48
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content
  • X. Zhu, P. Milanfar
  • Computer Science, Mathematics
  • IEEE Transactions on Image Processing
  • 1 December 2010
Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choiceExpand
  • 333
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NIMA: Neural Image Assessment
  • H. Talebi, P. Milanfar
  • Computer Science, Medicine
  • IEEE Transactions on Image Processing
  • 15 September 2017
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storageExpand
  • 165
  • 38
The Little Engine That Could: Regularization by Denoising (RED)
Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led some to believe thatExpand
  • 206
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Fast and Robust Multi-Frame Super-Resolution
In the last two decades, many papers have been published, proposing a variety of methods for multiframe resolution enhancement. These methods are usually very sensitive to their assumed model of dataExpand
  • 318
  • 34
Advances and challenges in super‐resolution
Super‐Resolution reconstruction produces one or a set of high‐resolution images from a sequence of low‐resolution frames. This article reviews a variety of Super‐Resolution methods proposed in theExpand
  • 589
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