Low Light Image Enhancement using MIRNet

MD Sana Meharaj; Esampelli Malathi; Chittumalla Keerthana; Poosala Harshitha; Dasari Mahesh Kumar

doi:10.36948/ijfmr.2023.v05i03.4001

Low Light Image Enhancement using MIRNet

Author(s)	MD Sana Meharaj, Esampelli Malathi, Chittumalla Keerthana, Poosala Harshitha, Dasari Mahesh Kumar
Country	India
Abstract	With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN- based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present a novel architecture with the collective goals of maintaining spatiallyprecise high- resolution representations through the entire network, and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi- scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution and image enhancement.
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-26
Cite This	Low Light Image Enhancement using MIRNet - MD Sana Meharaj, Esampelli Malathi, Chittumalla Keerthana, Poosala Harshitha, Dasari Mahesh Kumar - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.4001
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.4001
Short DOI	https://doi.org/gsd5bv

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Low Light Image Enhancement using MIRNet

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