Preserving quality in minimum frame selection within multi-frame super-resolution

Rahimi, Akbar; Moallem, Payman; Shahtalebi, Kamal; Momeni, Mehdi

doi:10.1016/j.dsp.2017.09.011

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Kang [22] proposed a robotic imaging system for selecting the best shot among several portraits. Rahimi et al [23] selected the minimum number of frames, i.e., keyframes, required in a high resolution (HR) imaging task. Jeyabharathi and Djey [24] extracted the video background using a cut set by selecting keyframes from a sequence of frames.…”

Section: Literature Reviewmentioning

confidence: 99%

Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

et al. 2022

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In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in various fields. Due to the high volume of information in a video, its processing is still expensive for most hardware systems, mainly in terms of its required runtime and memory. Hence, the optimal selection of keyframes to minimize redundant information in video processing systems has become noteworthy in facilitating this problem. Eliminating some frames can simultaneously reduce the required computational load, hardware cost, memory and processing time of intelligent video-based systems. Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Some HAR methods are trained with videos processed by the proposed method to assess its efficiency. The results demonstrate that the application of the proposed method increases the accuracy of the HAR system by up to 3% compared to random image selection and cropping methods. Additionally, for most cases, the proposed method reduces the training time of the used machine learning algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

et al. 2022

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“…Although there are many quality evaluation methods [ 24 , 25 , 26 ] for image selection, most of them are computationally intensive. We need a simple and effective method to quickly select appropriate LR images, so we propose a method of minimum variance.…”

Section: Proposed Mfsr Algorithmmentioning

confidence: 99%

Robust Multi-Frame Super-Resolution Based on Adaptive Half-Quadratic Function and Local Structure Tensor Weighted BTV

Liu

Wang

Huang

et al. 2021

Sensors

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It is difficult to improve image resolution in hardware due to the limitations of technology and too high costs, but most application fields need high resolution images, so super-resolution technology has been produced. This paper mainly uses information redundancy to realize multi-frame super-resolution. In recent years, many researchers have proposed a variety of multi-frame super-resolution methods, but it is very difficult to preserve the image edge and texture details and remove the influence of noise effectively in practical applications. In this paper, a minimum variance method is proposed to select the low resolution images with appropriate quality quickly for super-resolution. The half-quadratic function is used as the loss function to minimize the observation error between the estimated high resolution image and low-resolution images. The function parameter is determined adaptively according to observation errors of each low-resolution image. The combination of a local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge preserves the details of the image and suppresses the noise simultaneously. The experimental results on synthetic data and real data show that our proposed method can better preserve the details of the image than the existing methods.

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“…Recovering more resolute scenes from their degraded versions has been a long-standing challenge that attracts the attention of most scholars. One approach to address the challenge is called MSR, which uses multiple frames of degraded images to reconstruct a high-resolution image [29][30][31]. Laghrib et al applied a non-local form of the bilateral total variation that takes into consideration complex spatial interactions within images [26].…”

Section: Multiframe Super-resolution (Msr)mentioning

confidence: 99%

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

et al. 2020

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Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

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Preserving quality in minimum frame selection within multi-frame super-resolution

Cited by 4 publications

References 19 publications

Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition

Robust Multi-Frame Super-Resolution Based on Adaptive Half-Quadratic Function and Local Structure Tensor Weighted BTV

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

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