MdVRNet: Deep Video Restoration under Multiple Distortions

Rota, Claudio; Buzzelli, Marco

doi:10.5220/0010828900003124

Cited by 3 publications

(2 citation statements)

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“…Therefore, designing robust all-in-one methods that can address multiple restoration tasks at the same time, i.e., restoring videos containing multiple distortion types, would extend their applicability to real-world cases. Some methods towards this direction have been recently developed (Rota et al 2022;Katsaros et al 2021).…”

Section: All-in-one Video Restoration Methodsmentioning

confidence: 99%

Video restoration based on deep learning: a comprehensive survey

et al. 2022

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Video restoration concerns the recovery of a clean video sequence starting from its degraded version. Different video restoration tasks exist, including denoising, deblurring, super-resolution, and reduction of compression artifacts. In this paper, we provide a comprehensive review of the main features of existing video restoration methods based on deep learning. We focus our attention on the main architectural components, strategies for motion handling, and loss functions. We analyze the standard benchmark datasets and use them to summarize the performance of video restoration methods, both in terms of effectiveness and efficiency. In conclusion, the main challenges and future research directions in video restoration using deep learning are highlighted.

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Section: All-in-one Video Restoration Methodsmentioning

confidence: 99%

Video restoration based on deep learning: a comprehensive survey

et al. 2022

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“…The LIVE Multiply Distorted Image Quality Database (LIVEM) was collected with the specific goal of monitoring the quality of visual content that may be corrupted by multiple distortions [45], a line of research correlated with the increasing efforts to improve bandwidth usage in more realistic scenarios [50]. The dataset collects opinion scores from 37 subjects, for a total of 8880 judgments on 15 pristine reference images and 405 multiply-distorted images of two types: blur followed by JPEG, and blur followed by noise.…”

Section: ) Livemmentioning

confidence: 99%

Full-Reference Image Quality Expression via Genetic Programming

Bakurov

Buzzelli

Schettini

et al. 2023

IEEE Trans. on Image Process.

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Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.

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Deep Learning Enabled Video Semantic Transmission Against Multi-Dimensional Noise

Niu,

Wang,

et al. 2023

2023 IEEE Globecom Workshops (GC Wkshps)

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MdVRNet: Deep Video Restoration under Multiple Distortions

Cited by 3 publications

References 0 publications

Video restoration based on deep learning: a comprehensive survey

Video restoration based on deep learning: a comprehensive survey

Full-Reference Image Quality Expression via Genetic Programming

Deep Learning Enabled Video Semantic Transmission Against Multi-Dimensional Noise

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