INSTITUTIONAL DIGITAL REPOSITORY

Browsing Year-2021 by Author "Murala, S."

Browsing Year-2021 by Author "Murala, S."

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  • DarkGAN: Night image enhancement using generative adversarial networks 
    Alaspure, P.; Hambarde, P.; Dudhane, A.; Murala, S. (2022-12-20)
    Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data ...
  • Deep adversarial network for scene independent moving object segmentation 
    Patil, P. W.; Dudhane, A.; Murala, S.; Gonde, A. B. (2021-08-01)
    The current prevailing algorithms highly depend on additional pre-trained modules trained for other applications or complicated training procedures or neglect the inter-frame spatiotemporal structural dependencies. Also, ...
  • Diverse receptive field based adversarial concurrent encoder network for image inpainting 
    Phutke, S. S.; Murala, S. (2021-12-06)
    Image inpainting is nowadays demanding because of its wide applications such as removing the unwanted objects from the image or recovering the old corrupted photo. Existing approaches achieved superior performance with ...
  • Hyperrealistic image inpainting with hypergraphs 
    Wadhwa, G.; Dhall, A.; Murala, S.; Tariq, U. (2021-12-06)
    Image inpainting is a non-trivial task in computer vision due to multiple possibilities for filling the missing data, which may be dependent on the global information of the image. Most of the existing approaches use the ...
  • Motion estimation in hazy videos 
    Chaudhary, S.; Dudhane, A.; Patil, P. W.; Murala, S.; Talbar, S. (2021-10-27)
    Motion estimation is the basic need for the success of many video analysis algorithms such as moving object detection, human activity recognition, etc. Most of the motion estimation algorithms are prone to weather ...
  • MSAR-Net: Multi-scale attention based light-weight image super-resolution 
    Mehta, N.; Murala, S. (2021-10-25)
    Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The ...
  • Multi-frame recurrent adversarial network for moving object segmentation 
    Patil, P. W.; Dudhane, A.; Murala, S. (2021-12-06)
    Moving object segmentation (MOS) in different practical scenarios like weather degraded, dynamic background, etc. videos is a challenging and high demanding task for various computer vision applications. Existing ...
  • An unified recurrent video object segmentation framework for various surveillance environments 
    Patil, P.W.; Dudhane, A.; Kulkarni, A.; Murala, S.; Gonde, A.B.; Gupta, S. (2022-09-03)
    Moving object segmentation (MOS) in videos received considerable attention because of its broad security-based applications like robotics, outdoor video surveillance, self-driving cars, etc. The current prevailing algorithms ...
  • UW-GAN: single image depth estimation and image enhancement for underwater images 
    Hambarde, P.; Murala, S.; Dhall, A. (2021-12-06)
    Due to the unavailability of large-scale underwater depth image datasets and ill-posed problems, underwater single-image depth prediction is a challenging task. An unambiguous depth prediction for single underwater image is ...

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