Multi-Frame Super-Resolution: A Survey

Khattab, Mahmoud M.; Zeki, Akram M.; Alwan, Ali A.; Badawy, Ahmed Said; Thota, Lalitha Saroja

doi:10.1109/iccic.2018.8782382

Cited by 16 publications

(10 citation statements)

References 54 publications

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“…Polyester is more rigid, but one may assume that detecting this difference from folds was difficult at the resolution used in this study. Finer features might be detected by combining super-resolution processing of continuously captured images 14 combined with multi-resolution recognition, which is a topic for future study.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of clothing materials from smartphone camera images

Koike

Yamada

2023

Elect Comm in Japan

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Section: Discussionmentioning

confidence: 99%

Discrimination of clothing materials from smartphone camera images

Koike

Yamada

2023

Elect Comm in Japan

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“…Moreover, the SSIM value varies from zero to one so that the higher the SSIM value, the higher the similarity. Thus, reconstructed images are more effective [39,40].…”

Section: Performance Analysismentioning

confidence: 99%

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

Khattab¹,

Zeki²,

Alwan³

et al. 2023

Computer Systems Science and Engineering

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The prime purpose for the image reconstruction of a multi-frame superresolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes. Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework. In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts. The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.

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“…Although there are more methods proposed for MFSR in nature image processing [53], as of now, there is barely no network focus on multiple LR samples learning for HSIs, and the reconstruction is concentrated upon a limited degradation model only.…”

Section: Multi-frame Image Super-resolutionmentioning

confidence: 99%

Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution

Deng

Luo

Wang

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral Imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging conditions, restricting the subsequent object detection applications. At present, the Single Hyperspectral Super Resolution (SHSR) strategy has encountered the bottleneck on more precise details extraction, and the Fusion Hyperspectral Image Super Resolution (FHSR) strategy must need extra RGB/multispectral information which is not suitable for general HSI usage. Also, both types of current strategies focus less on the multiple degradation causes of low spatial resolution. In this paper, a step forward in designing a novel framework of multiple frame splicing strategy to greatly improve the SHSR quality, and applying multiple HSI degradation models to better fit the real degradation circumstance. Specifically, the framework is an end-to-end Super Resolution (SR) network that supersedes a single up-sampling module and removes complex attention residual model due to the same size of multiple splicing low-resolution input samples with high-resolution outputs. The effective framework will alleviate the vague at higher multiples, and accelerate the training convergence. Based on this framework, multiple degradation lowresolution samples can be simultaneously combined to fit better for the blind super-resolution result. Concretely, the degradation focus on the blur, noise, compression, and their combinations to simulate the real degradation. Experimental results on three different hyperspectral datasets demonstrate that the proposed MFSDM algorithm can significantly enhance the details in the recovered high-resolution hyperspectral images, and outperforms the state-of-the-art SHSR methods.

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Multi-Frame Super-Resolution: A Survey

Cited by 16 publications

References 54 publications

Discrimination of clothing materials from smartphone camera images

Discrimination of clothing materials from smartphone camera images

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution

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