Special lssues

Deep Learning for Image Video Restoration and Compression

Submission Deadline: 12 October 2022 (closed)

Guest Editors

Dr. Mahendrakhan M, Hindusthan Institute of Technology, India.
Dr. Paulchamy Balaiyah, Hindusthan Institute of Technology, India.
Dr. Uma Maheshwari, Institute of Technology, India.


The huge success of deep-learning-based approaches in computer vision motivated research in learned solutions to classic image/video processing issues, such as denoising, deblurring, super-resolution, and compression. Hence, learning based approaches have emerged as a promising nonlinear signal processing platform for image/video reconstruction and compression. Latest works have shown that trained models can make substantial performance improvements over conventional approaches. Hence, the state of the art of image reconstruction and compression is being redefined. However, there appear to be overcome compelling research obstacles. These include: I learned models include millions of parameters, which puts the inference of common devices into real time a challenge; ii) it is difficult to understand learned models or to achieve results; iii) it is important to have a loss role in training, which accurately reflects the human perception of quality; This special issue calls for creative architectures and training approaches for powerful image and video restore networks and compression to address these and other challenges.


● New architectures for image and video restoration, including super-resolution, denoising, deblurring, dehazing, and inpainting.
● Novel learned methods for motion compensation and image/video compression.
● Computationally efficient networks for image/video restoration and compression.
● Explainable deep learning for image/video restoration and compression.
● Training with novel loss functions that accurately reflects human perception of quality.
● Robust methods on real-world image/video, where the training data is noisy and/or available training data is limited.

Published Papers

  • Open Access


    Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

    Seema Sabharwal, Priti Singla
    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2567-2582, 2023, DOI:10.32604/iasc.2023.035497
    (This article belongs to this Special Issue: Deep Learning for Image Video Restoration and Compression)
    Abstract Sign language is used as a communication medium in the field of trade, defence, and in deaf-mute communities worldwide. Over the last few decades, research in the domain of translation of sign language has grown and become more challenging. This necessitates the development of a Sign Language Translation System (SLTS) to provide effective communication in different research domains. In this paper, novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm (Hybrid-AO) for image segmentation is proposed for the translation of alphabet-level Indian Sign Language (ISLTS) with a 5-layer Convolution Neural Network (CNN). The focus of this paper is to analyze various… More >

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