INSTITUTIONAL DIGITAL REPOSITORY

Browsing by Author "Dudhane, A."

Browsing by Author "Dudhane, A."

Sort by: Order: Results:

  • Adaptive Feature Consolidation Network for Burst Super-Resolution 
    Mehta, N.; Dudhane, A.; Murala, S.; Zamir, S.W.; Khan, s; Khan, F.S. (2022-10-20)
    Modern digital cameras generally count on image signal processing (ISP) pipelines for producing naturalistic RGB images. Nevertheless, in comparison to DSLR cameras, low-quality images are generally output from portable mobile ...
  • C2MSNet: a novel approach for single image haze removal 
    Dudhane, A.; Murala, S. (2018-10-08)
    Degradationofimagequalityduetothepresenceofhaze is a very common phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand crafted haze relevantfeatures. However,thesemethodshavetheproblem of color ...
  • Cardinal color fusion network for single image haze removal 
    Dudhane, A.; Murala, S. (2021-08-25)
    Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct ...
  • CDNet: single image de-hazing using unpaired adversarial training 
    Dudhane, A.; Murala, S. (2021-08-25)
    Outdoor scene images generally undergo visibility degradation in presence of aerosol particles such as haze, fog and smoke. The reason behind this is, aerosol particles scatter the light rays reflected from the object ...
  • 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, ...
  • Deep network for extremely low-resolution human action recognition 
    Chaudhary, S.; Patil, P.W.; Dudhane, A.; Murala, S. (2022-12-09)
    Due to advancement in automated applications, privacy-preserving is an emerging concern. This concern is more significant in the case of human-centred surveillance application like human action recognition (HAR). Along ...
  • Deep underwater image restoration and beyond 
    Dudhane, A.; Hambarde, P.; Patil, P.; Murala, S. (2021-07-01)
    Underwater image restoration is a challenging problem due to the multiple distortions. Degradation in the information is mainly due to the 1) light scattering effect 2) wavelength dependent color attenuation and 3) object ...
  • An End-to-End edge aggregation network for moving object segmentation 
    Patil, P. W.; Biradar, K. M.; Dudhane, A.; Murala, S. (2021-07-04)
    Moving object segmentation in videos (MOS) is a highly demanding task for security-based applications like automated outdoor video surveillance. Most of the existing techniques proposed for MOS are highly depend on ...
  • An End-to-End network for image De-Hazing and beyond 
    Dudhane, A.; Patil, P. W.; Murala, S. (2021-07-04)
    Degradation in the quality of images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion ...
  • An End-to-End Network for Image De-Hazing and Beyond 
    Dudhane, A.; Patil, P.W.; Murala, S. (2022-06-23)
    Degradation in the qualityof images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion and ...
  • End-to-End recurrent generative adversarial network for traffic and surveillance applications 
    Patil, P. W.; Dudhane, A.; Murala, S. (2021-06-19)
    In video frame segmentation, many existing deep networks and contemporary approaches give a remarkable performance with the assumption that the only foreground is moving, and the background is stationary. However, in the ...
  • Image and video super resolution using recurrent generative adversarial network 
    Thawakar, O.; Patil, P. W.; Dudhane, A.; Murala, S.; Kulkarni, U. (2019-12-23)
    Recently, the convolutional neural network with residual learning models achieves high accuracy for single image super-resolution with different scale factors. With adversarial learning model, effective learning of ...
  • Learning-based methods for single image haze removal 
    Dudhane, A. (2021-07-23)
    Haze is an atmospheric phenomenon where turbid media obscure the scenes. Haze reduces the visibility of the scenes and reduces the reliability of outdoor surveillance systems. Under severe hazy weather conditions, 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 ...
  • Motion saliency based generative adversarial network for underwater moving object segmentation 
    Patil, P. W.; Thawakar, O; Dudhane, A.; Murala, S. (2021-08-20)
    The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video ...
  • 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 ...
  • Multi‐frame based adversarial learning approach for video surveillance 
    Patil, P. W.; Dudhane, A.; Chaudhary, S.; Murala, S. (2021-12-19)
    Foreground-background segmentation (FBS) is one of the prime tasks for automated video-based applications like traffic analysis and surveillance. The different practical scenarios like weather degraded videos, irregular ...
  • NTIRE 2019 image dehazing challenge report 
    Ancuti, C. O.; Ancuti, C.; Timofte, R.; Gool, L. V.; Zhang, L.; Yang, M. H.; Guo, T.; Li, X.; Cherukuri, V.; Monga, V.; Jiang, H.; Yang, S.; Liu, Y.; Qu, X.; Wan, P.; Park, D.; Chun, S. Y.; Hong, M.; Huang, J.; Chen, Y.; Chen, S.; Wang, B.; Michelini, P. N.; Liu, H.; Zhu, D.; Liu, J.; Santra, S.; Mondal, R.; Chanda, B.; Morales, P.; Klinghoffer, T.; Quan, L. M.; Kim, Y. G.; Liang, X.; Li, R.; Pan, J.; Tang, J.; Purohit, K.; Suin, M.; Rajagopalan, A. N.; Schettini, R.; Bianco, S.; Piccoli, F.; Cusano, C.; Celona, L.; Hwang, S.; Ma, Y. S.; Byun, H.; Murala, S.; Dudhane, A.; Aulakh, H.; Zheng, T.; Zhang, T.; Qin, W.; Zhou, R.; Wang, S.; Tarel, J. P.; Wang, C.; Wu, J. (2021-08-21)
    This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze ...
  • Pose guided dynamic image network for human action recognition in person centric videos 
    Chaudhary, S.; Dudhane, A.; Patil, P.; Murala, S. (2021-08-20)
    The most emerging concerns in computer vision are size of data to process and privacy preserving of the end user. Camera sensors are all around us these days, recording and analysing our day-to-day activities. In this ...