Deep Networks for Image and Video Super-Resolution
- URL: http://arxiv.org/abs/2201.11996v1
- Date: Fri, 28 Jan 2022 09:15:21 GMT
- Title: Deep Networks for Image and Video Super-Resolution
- Authors: Kuldeep Purohit, Srimanta Mandal, A. N. Rajagopalan
- Abstract summary: Single image super-resolution (SISR) is built using efficient convolutional units we refer to as mixed-dense connection blocks (MDCB)
We train two versions of our network to enhance complementary image qualities using different loss configurations.
We further employ our network for super-resolution task, where our network learns to aggregate information from multiple frames and maintain-temporal consistency.
- Score: 30.75380029218373
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Efficiency of gradient propagation in intermediate layers of convolutional
neural networks is of key importance for super-resolution task. To this end, we
propose a deep architecture for single image super-resolution (SISR), which is
built using efficient convolutional units we refer to as mixed-dense connection
blocks (MDCB). The design of MDCB combines the strengths of both residual and
dense connection strategies, while overcoming their limitations. To enable
super-resolution for multiple factors, we propose a scale-recurrent framework
which reutilizes the filters learnt for lower scale factors recursively for
higher factors. This leads to improved performance and promotes parametric
efficiency for higher factors. We train two versions of our network to enhance
complementary image qualities using different loss configurations. We further
employ our network for video super-resolution task, where our network learns to
aggregate information from multiple frames and maintain spatio-temporal
consistency. The proposed networks lead to qualitative and quantitative
improvements over state-of-the-art techniques on image and video
super-resolution benchmarks.
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