Authors :
Dr. Hansaraj Wankhede; Abhishikth Thul; Deep Malviya; Pratik Pathe; Shivam Kuite
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/mv24yfax
DOI :
https://doi.org/10.5281/zenodo.8077259
Abstract :
Image restoration using deep learning
attempts to create an image recovery system that can
restore outdated and corrupted images regardless of the
type of noise present. Photos play an important role in
capturing and preserving cherished moments in today's
digital age. However, due to a variety of environmental
conditions, these images can get distorted over time.
Manual recovery is time-consuming and labor-intensive,
emphasizing the need for an automated alternative. Deep
learning techniques, notably convolutional neural
networks, are used in the proposed system, which has
demonstrated promising results in image processing
tasks. The essay goes over these approaches in detail,
focusing on image noise reduction, deblurring, dehazing,
and super-resolution. Different network topologies are
investigated, including those with residual or merge-skip
connections, as well as their receptive fields and the
usage of unsupervised autoencoder processes. The study
also looks at image quality criteria to see how helpful
they are in image recovery. An effective deblurring
network and adaptable training algorithms for highresolution recovery tasks are suggested to handle the
special difficulty of deblurring. The proposed method's
performance is compared to state-of-the-art approaches
utilizing both quantitative and qualitative assessments.
The study finishes with a discussion of prospective future
research topics and outstanding challenges in image
recovery. The ultimate goal of this research is to create a
robust and efficient image recovery system capable of
restoring photos to their original quality regardless of
the type or severity of corruption or noise present.
Keywords :
Image recovery, Noise reduction, Neural networks, Image restoration, Deep learning, Image processing, Super-resolution, Deblurring.
Image restoration using deep learning
attempts to create an image recovery system that can
restore outdated and corrupted images regardless of the
type of noise present. Photos play an important role in
capturing and preserving cherished moments in today's
digital age. However, due to a variety of environmental
conditions, these images can get distorted over time.
Manual recovery is time-consuming and labor-intensive,
emphasizing the need for an automated alternative. Deep
learning techniques, notably convolutional neural
networks, are used in the proposed system, which has
demonstrated promising results in image processing
tasks. The essay goes over these approaches in detail,
focusing on image noise reduction, deblurring, dehazing,
and super-resolution. Different network topologies are
investigated, including those with residual or merge-skip
connections, as well as their receptive fields and the
usage of unsupervised autoencoder processes. The study
also looks at image quality criteria to see how helpful
they are in image recovery. An effective deblurring
network and adaptable training algorithms for highresolution recovery tasks are suggested to handle the
special difficulty of deblurring. The proposed method's
performance is compared to state-of-the-art approaches
utilizing both quantitative and qualitative assessments.
The study finishes with a discussion of prospective future
research topics and outstanding challenges in image
recovery. The ultimate goal of this research is to create a
robust and efficient image recovery system capable of
restoring photos to their original quality regardless of
the type or severity of corruption or noise present.
Keywords :
Image recovery, Noise reduction, Neural networks, Image restoration, Deep learning, Image processing, Super-resolution, Deblurring.
