A compendious study of super-resolution techniques by single image

Pandey, Garima; Ghanekar, Umesh

doi:10.1016/j.ijleo.2018.03.103

Cited by 24 publications

(5 citation statements)

References 70 publications

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“…This reverse process is carried out with different algorithms developed for different applications. The techniques for SR are mainly classified by their domain: spatial, frequency, and wavelet [3][4][5][6]. Each domain has its advantages and disadvantages based on the application field.…”

Section: A Super-resolution With the Observation Modelmentioning

confidence: 99%

“…The techniques in the frequency field deal with the frequency element as an image trait. The frequency-domain approach is depending on shifting, aliasing, and band limitation of the signal [3][4][5][6]. The very first approach of SR was in the frequency area [7].…”

Section: B Super-resolution Process Categorizationmentioning

confidence: 99%

“…This leads to the SR concept and related developments. Many authors [1][2][3][4][5] explored the SR concept, its recent applications, limitations, and scope for improvements.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

Daithankar¹,

Ruikar²

2021

Eng. Technol. Appl. Sci. Res.

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The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.

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Section: A Super-resolution With the Observation Modelmentioning

confidence: 99%

Section: B Super-resolution Process Categorizationmentioning

confidence: 99%

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Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

Daithankar¹,

Ruikar²

2021

Eng. Technol. Appl. Sci. Res.

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“…Superresolution (SR) imaging is a class of techniques that enhance the resolution of an imaging system. In optical SR the diffraction limit of systems is transcended, while in geometrical SR the resolution of digital imaging sensors is enhanced [ 1 ]. Among many SR imaging methods, the compressed sensing (CS) method based on the principle of signal sparsity has attracted much research attention in recent years [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Optical Alignment Error on Compression Coding Superresolution Imaging

Wang

Xing

et al. 2022

Sensors

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Superresolution (SR) imaging technology based on compression coding has always been considered as the key to break through the geometric resolution of the detector. In addition to factors such as the reconstruction algorithm and mounting platform vibrations, the impact of inherent errors in the optical system itself on the reconstruction results of SR imaging is also obvious. To address this issue, a study on the design of the SR optical system and the influence of optical alignment errors on SR imaging was conducted. The design of the SR optical system based on digital micro-mirror device (DMD) for long-wave infrared wavelength was completed, and an athermal analysis of the system was carried out. The design results showed that the SR optical system has good imaging quality in the operating temperature range. The imaging model of the DMD SR imaging optical system is established according to the designed SR optical system. We investigated the influence of various alignment errors, including decenter, tilt, lens interval error and defocus, on the imaging properties of the SR optical system. Various random combinations of alignment errors were introduced into the optical system, respectively, and the SR reconstructed image quality of the imaging system was analyzed using the inverse sensitivity method to obtain the tolerance limits when the system was assembled. Finally, the effectiveness of the method to obtain the alignment tolerance limit of the compression coding SR imaging optical system was verified through a desktop demonstration experiment.

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“…In recent years, learning-based SR methods [3] have been extensively studied, which use a learned co-occurrence to predict the correspondence between LR and HR patches. The learning algorithms including Markov network [4,5,6], neighbor embedding [7,8,9,10], dictionary learning [11,12,13,14], anchored neighborhood regression [16,15], random forests [17], and deep learning [18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Liao

Bai

Zhang

et al. 2019

cai

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Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A subdictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.

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A compendious study of super-resolution techniques by single image

Cited by 24 publications

References 70 publications

Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

The Influence of Optical Alignment Error on Compression Coding Superresolution Imaging

Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

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