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Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression...
Here we propose an efficient estimation method to interpolate new samples in a blurred and aliased observation, in such a way that (1) aliasing artifacts in an ulterior restoration are mitigated, and (2) thanks to aliasing we may recover some spatial frequencies beyond the Nyquist frequency (super-resolution). The only requirement is having a good approximation of the blurring kernel in high resolution...
Convolutional neural networks (CNN) have been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of compressed video super-resolution. Traditional SR algorithms for compressed videos rely on information from the encoder such as frame type or quantizer step, whereas our algorithm only requires the compressed low resolution...
Super-resolution (SR) algorithms for video sequences with high resolution (HR) guide frames can provide outstanding performances. Non-local means (NLM) algorithm compares the similarity between a pixel and its neighbors. NLM replaces every pixel with a weighted average of its neighbors. The NLM based SR algorithm can super-resolve low resolution (LR) frames using the HR guide frames in the video sequence...
Automated classification of HEp-2 cell images is crucial for fast and accurate detection of autoimmune diseases. Recent competitions resulted in high classification rates on publicly available datasets. However, performance on low-resolution HEp-2 images typically lagged behind that of high-resolution images due to the blurring and sub-sampling of fine cellular details. Direct interpolation of low-resolution...
In this paper, we present a novel structure preserving method for single image super-resolution to well construct edge structures and small detail structures. In our approach, the sharp edges are recovered via a novel edge preserving interpolation technique based on a well estimated gradient field and the edge preserving method, which incorporate the local and non-local structure information. The...
Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the “right” exemplars defined and identified? This...
In this paper, we show how to boost the performance and speed of the Simple Functions (SF) algorithm for single-image superresolution [1]. This method partitions the low-resolution patch space and learns linear regressors to map low-resolution to highresolution patches. We optimize the partitioning of the patch feature space by first employing dimensionality reduction and then explicitly minimizing...
In this paper, we consider the problem of example based single image super-resolution. Our main contribution is introducing a new framework that makes no assumption about the structural similarity between the high-resolution (HR) and low-resolution (LR) manifolds. Instead, we use a subspace affinity measure to exploit the similarity between each HR and LR subspace. First, we train both LR and HR manifolds...
Recently, single image super-resolution (SISR) is very important research field to reconstruct a high-resolution (HR) image from a low-resolution (LR) image. However, existing image super-resolution approaches require a lot of computations or consider parameters for various situations. This paper proposes an efficient and simple image super-resolution technique using multiple directional lapped orthogonal...
An image taken under extremely low illumination is modeled as obeying the Poissonian-Gaussian distribution. This paper proposes a method to recover a higher-quality color moving-image sequence from Poissonian-Gaussian observations. The method performs virtual multiplex imaging, formed as a series of pixel binning and redundant subsampling, on the input sequence to increase its statistical reliability,...
Capturing multiple images using the burst mode of handheld cameras can be a boon to obtain a high resolution (HR) image by exploiting the subpixel motion among the captured images arising from handshake. However, the caveat with mobile phone cameras is that they produce rolling shutter (RS) distortions that must be accounted for in the super-resolution process. We propose a method in which we obtain...
Super-resolution (SR) offers an effective approach to boost quality and details of low-resolution (LR) images to obtain high-resolution (HR) images. Despite the theoretical and technical advances in the past decades, it still lacks plausible methodology to evaluate and compare different SR algorithms. The main cause to this problem lies in the missing ground truth data for SR. Unlike in many other...
In this paper, a non-local based sparse representation (called as the NLSR) is proposed for the super-resolution of hyperspectral image. Specifically, the NLSR firstly uses the non-local Kmeans to partition pixels of low spatial resolution hyperspectral image into several classes. The non-local Kmeans can exploit the similar patterns and structures of the low spatial resolution image to enhance the...
In this communication, a fast reconstruction algorithm is proposed for fluorescence blind structured illumination microscopy (SIM) under the sample positivity constraint. This new algorithm is by far simpler and faster than existing solutions, paving the way to 3D and real-time 2D reconstruction.
This paper introduces a novel system for the analysis of superresolution microscopy images using a learning based approach boosting performance and simplicity of use. Key component of single-molecule-localisation (SML) microscopy techniques is the ability to localise single emitting molecules in a stack of noisy images with a high degree of accuracy. To this end, we propose a SVM-based detector coupled...
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