Image Super-resolution Reconstruction Method Research Articles

Super-resolution Techniques based on Temporal Recursion using Optimal and Real Parameters

The major goal of a super-resolution image reconstruction method is to construct a single in-depth high-resolution image from a group of numerous low-resolution images of the scene taken from diverse positions. Since every low-resolution image retains a different view of the scene, it is possible to reconstruct an in-depth high-resolution image. Thus image super-resolution is a key to overcoming the material precincts of hardware competence. An enormous amount of video is still in traditional formats or at an even lower resolution. Some also has relentless coding artifacts. Hence, there is a need for techniques that can improve video quality and that show all the traditional and low-resolution videos on panels with high-resolution grids. Iterative super-resolution reconstruction algorithms can accomplish this exigent chore by using internal image models and an incorporated feedback loop to control output quality, thereby improving resolution and lessening artifacts. This article portrays the prospects of iterative reconstruction algorithms and establishes a new super-resolution algorithm that is computationally very strong and efficient against motion estimation errors.

A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment

A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low‐resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High‐resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal‐to‐noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill‐posed problems in reconstruction process.

Single image super-resolution by clustered sparse representation and adaptive patch aggregation

A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, nonlocal self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.

MAP-regularized robust reconstruction for underwater imaging detection

In order to enhance the visual quality of underwater images, applications such as enhancement and restoration can be applied, but the resolution is still limited. Super-resolution reconstruction is a widely used technique for improving resolution beyond the limit of imaging system. With knowledge of the point spread function and techniques of regularization, the performance of reconstruction can be further enhanced. The presented effort proposed a robust image super-resolution reconstruction method under maximum a posteriori framework with regularization by the point spread function for underwater imaging detection. Objective image quality metrics are used to quantify the effectiveness of the reconstruction. Experimental results showed that the proposed method can effectively improve the resolution and quality of underwater imaging detection.

Study of Supper Resolution Processing Methods for Thick Pinhole Image

An image super resolution reconstruction method was used to improve the spatial resolution of the thick pinhole imaging system and to mitigate the limitations of the image spatial resolution of the hardware of the image diagnostic system. The thick pinhole is usually applied into the diagnostics of the high energy neutron radiation image. Due to the impacts among its energy flux, spatial resolution and effective field of view, in dealing with the large area radiation source, the spatial resolution of the thick pinhole neutron image cannot meet the requirements for high precision modeling of the radiation source image. In this paper, the Lucy-Richardson image super resolution reconstruction method was used to simulate the thick pinhole imaging and super resolution image reconstruction. And the spatial resolution of the image could be increased by over three times after the image super resolution reconstruction. Besides, in dealing with the pseudo-noise, plum blossom shape appeared in the image super resolution reconstruction. The analysis of the source of the pseudo-noise was made based on the simulation of the image reconstruction under various conditions according to the characteristics of the thick pinhole image configuration.

Fast MAP-based super-resolution algorithm using multilevel interpolation and reconstruction

Super-resolution (SR) image reconstruction can produce a high-resolution (HR) digital image from multiple low-resolution (LR) photographs. Many applications require HR images, including medical imaging, satellite imaging, and video applications. Recovering lost details from down-sampled images is the main challenge of SR techniques. However, most studies have not taken computational complexity into consideration. Therefore, this article presents a new fast maximum a posteriori (MAP)-based SR image reconstruction method based on a multilevel algorithm. In particular, this work focuses on the case of input LR images that do not meet the requirements for the analysis. A two-step interpolation process is proposed to increase the quality of the constructed SR image. Experimental results show that the proposed algorithm reduces the blocking artifacts in the reconstruct SR image caused by a lack of LR images. Compared to the conventional MAP-based method, the proposed method also achieves up to 60% reduction in computational complexity with comparable peak signal-to-noise ratio and visual quality.

Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing

In this paper, the potential of superresolution (SR) image reconstruction methods for sub-pixel land-cover mapping in dense urban areas is studied. A multiple endmember approach (MESMA) is used for unmixing both original hyperspectral CHRIS/Proba and SR enhanced CHRIS/Proba data. Validation based on high resolution orthophotos (25cm) shows that land-cover fraction maps generated from SR-enhanced CHRIS/Proba data (9m) have a lower overall fractional error compared to the land-cover fractions produced from the original CHRIS data (18m), when validating both results at the 18m resolution. Validation of SR results at the 9m resolution produces an overall mean absolute error (OMAE) of 16.7% compared to an OMAE of 14.3% at the 18m resolution, with the original data, yet the impervious surface map produced at 9m has a much higher level of detail than the original map, better representing the built-up pattern of the urban environment. Detailed analysis of impervious surface mapping results for different reference proportion intervals points at smaller average fractional errors for impervious surface fractions produced from SR-enhanced data when validation is done at the original 18m resolution, over the entire range of proportions. Only for pixels not containing any impervious surface cover the fractional error is marginally higher than with the original data. These results demonstrate the potential of SR-enhanced data for more accurate impervious surface mapping in dense and heterogeneous urban areas.

Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction

Recent researches have shown that the sparse representation based technology can lead to state of art super-resolution image reconstruction (SRIR) result. It relies on the idea that the low-resolution (LR) image patches can be regarded as down sampled version of high-resolution (HR) images, whose patches are assumed to have a sparser presentation with respect to a dictionary of prototype patches. In order to avoid a large training patches database and obtain more accurate recovery of HR images, in this paper we introduce the concept of examples-aided redundant dictionary learning into the single-image super-resolution reconstruction, and propose a multiple dictionaries learning scheme inspired by multitask learning. Compact redundant dictionaries are learned from samples classified by K-means clustering in order to provide each sample a more appropriate dictionary for image reconstruction. Compared with the available SRIR methods, the proposed method has the following characteristics: (1) introducing the example patches-aided dictionary learning in the sparse representation based SRIR, in order to reduce the intensive computation complexity brought by enormous dictionary, (2) using the multitask learning and prior from HR image examples to reconstruct similar HR images to obtain better reconstruction result and (3) adopting the offline dictionaries learning and online reconstruction, making a rapid reconstruction possible. Some experiments are taken on testing the proposed method on some natural images, and the results show that a small set of randomly chosen raw patches from training images and small number of atoms can produce good reconstruction result. Both the visual result and the numerical guidelines prove its superiority to some start-of-art SRIR methods.

Stochastic super-resolution image reconstruction

The objective of super-resolution (SR) imaging is to reconstruct a single higher-resolution image based on a set of lower-resolution images that were acquired from the same scene to overcome the limitations of image acquisition process for facilitating better visualization and content recognition. In this paper, a stochastic Markov chain Monte Carlo (MCMC) SR image reconstruction approach is proposed. First, a Bayesian inference formulation, which is based on the observed low-resolution images and the prior high-resolution image model, is mathematically derived. Second, to exploit the MCMC sample-generation technique for the stochastic SR image reconstruction, three fundamental issues are observed as follows. First, since the hyperparameter value of the prior image model controls the degree of regularization and intimately affects the quality of the reconstructed high-resolution image, how to determine an optimal hyperparameter value for different low-resolution input images becomes a very challenging task. Rather than exploiting the exhaustive search, an iterative updating approach is developed in this paper by allowing the value of hyperparameter being simultaneously updated in each sample-generation iteration. Second, the samples generated during the so-called burn-in period (measured in terms of the number of samples initially generated) of the MCMC-based sample-generation process are considered unreliable and should be discarded. To determine the length of the burn-in period for each set of low-resolution input images, a time-period bound in closed form is mathematically derived. Third, image artifacts could be incurred in the reconstructed high-resolution image, if the number of samples (counting after the burn-in period) generated by the MCMC-based sample-generation process is insufficient. For that, a variation-sensitive bilateral filter is proposed as a ‘complementary’ post-processing scheme, to improve the reconstructed high-resolution image quality, when the number of samples is insufficient. Extensive simulation results have clearly shown that the proposed stochastic SR image reconstruction method consistently yields superior performance.

Infrared image enhancement and human detection erformance measures

The ability to detect and recognise dangerous objects at a safe distance is a very important task in a number of defence, police and security applications. In this paper, we look at ways of increasing the effectiveness of infrared imagery for object recognition through processes such as super-resolution image reconstruction and deconvolution methods. In this paper, we propose two techniques for assessing image quality improvement: operator assessment and edge detection; and report on some initial work recently undertaken.

Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?

The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more cer- tainty is important. In this paper, super-resolution image reconstruction and deconvolution methods are explored, with an emphasis on quantifying and understanding human operator detection performance.

Improving Infrared Images for Standoff Object Detection

The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more certainty is important. Super-resolution image reconstruction and deconvolution methods are explored, as well as performance improvement measures.

Gibbs artifact reduction for POCS super-resolution image reconstruction

The topic of super-resolution image reconstruction has recently received considerable attention among the research community. Super-resolution image reconstruction methods attempt to create a single high-resolution image from a number of low-resolution images (or a video sequence). The method of projections onto convex sets (POCS) for super-resolution image reconstruction attracts many researchers’ attention. In this paper, we propose an improvement to reduce the amount of Gibbs artifacts presenting on the edges of the high-resolution image reconstructed by the POCS method. The proposed method weights the blur PSF centered at an edge pixel with an exponential function, and consequently decreases the coefficients of the PSF in the direction orthogonal to the edge. Experiment results show that the modification reduces effectively the visibility of Gibbs artifacts on edges and improves obviously the quality of the reconstructed high-resolution image.

High Dynamic Range Image Reconstruction with Spatial Resolution Enhancement

For the last two decades, two related approaches have been studied independently in conjunction with limitations of image sensors. The one is to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) observations suffering from various degradations such as blur, geometric deformation, aliasing, noise, spatial sampling and so on. The other one is to reconstruct a high dynamic range (HDR) image from differently exposed multiple low dynamic range (LDR) images. LDR is due to the limitation of the capacitance of analogue-to-digital converter and the nonlinearity of the imaging system's response function. In practical situations, since observations suffer from limitations of both spatial resolution and dynamic range, it is reasonable to address them in a unified context. Most super-resolution (SR) image reconstruction methods that enhance the spatial resolution assume that the dynamic ranges of observations are the same or the imaging system's response function is already known. In this paper, the conventional approaches are overviewed and the SR image reconstruction, which simultaneously enhances spatial resolution and dynamic range, is proposed. The image degradation process including limited spatial resolution and limited dynamic range is modelled. With the observation model, the maximum a posteriori estimates of the response function of the imaging system as well as the single HR image and HDR image are obtained. Experimental results indicate that the proposed algorithm outperforms the conventional approaches that perform the HR and HDR reconstructions sequentially with respect to both objective and subjective criteria.

A Regularized Super-resolution Image Reconstruction Method

A Regularized Super-resolution Image Reconstruction Method