Video Super Resolution: A Review

Daithankar, Mrunmayee V.; Ruikar, Sachin D.

doi:10.1007/978-981-15-1420-3_51

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Evaluation index: the image quality evaluation indexes used in this experiment are full reference image quality evaluation indexes: peak signal-to-noise ratio (PNSR) and structural similarity (SSIM) (Daithankar and Ruikar, 2020).…”

Section: Analysis Of the Super-resolution Resultsmentioning

confidence: 99%

“…Compared with SISR, MISR methods have the advantage of combining spatial and temporal information from sequence images . Traditional MISR methods based on spatial and frequency domain, are not only unable to deal with complex motions but also have problems with huge computation (Tom and Katsaggelos, 1995;Li et al, 2001;Daithankar and Ruikar, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

Shen

Liu

Yao

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.

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Section: Analysis Of the Super-resolution Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

Shen

Liu

Yao

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…it has no particular solution and it doesn't have a particular mathematical expression. Researchers have tried to convert this ill-posed problem into a well-posed one in order to get a solution [1,2]. Even these trials do not represent the exact issue but helped obtain nearby versions and their solutions.…”

Section: A Super-resolution With the Observation Modelmentioning

confidence: 99%

“…where x represents the original image or frame, y denotes the matrix data of the corrupted image or frame, m gives the number of the pixel lines of the images, and n signifies the number of columns in the pixel image [1].…”

Section: A Peak Signal To Noise Ratio (Psnr)mentioning

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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Specific Wavelet Family Selection for Wavelet Domain-Based Super-Resolution Application

Daithankar¹,

Ruikar²

2022

ICT Infrastructure and Computing

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Video Super Resolution: A Review

Cited by 11 publications

References 24 publications

Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

Comparison of Multi-Images Deep Learning Super Resolution for Passive Microwave Images of Arctic Sea Ice

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

Specific Wavelet Family Selection for Wavelet Domain-Based Super-Resolution Application

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