Image Restoration using Deep Learning Techniques
Akurathi Aravinda1, Challagulla Yoshitha2, Kakarla Meghana3, Kandula Sreeja4, B.Tejaswi5
1Akurathi Aravinda, B.Tech, Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad (Telangana), India.
2Challagulla Yoshitha*, B.Tech, Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad (Telangana), India.
3Kakarla Meghana, B.Tech, Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad (Telangana), India.
4Kandula Sreeja, B.Tech, Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad (Telangana), India.
5B.Tejaswi, Assistant Professor, Department of Computer Science, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 06 April 2022. | Revised Manuscript received on 12 April 2022. | Manuscript published on 30 June 2022. | PP: 13-16 | Volume-11 Issue-5, June 2022. | Retrieval Number: 100.1/ijeat.E35090611522 | DOI: 10.35940/ijeat.E3509.0611522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the modern era, due to the emergence of various technologies, most of the human work is now being performed by the computer system. The computer’s capacity to make everything possible is increasing as by the time. Photos are used to capture or freeze the moments in one’s life. We can embrace those moments at any time by looking at the pictures. It is natural that, as time passes by, these photos gets damaged due to environmental conditions that leads to loss of our important moments. Hence, preserving the photos is as important as taking them. The process of taking corrupt or noisy image and estimating the clean, original image is image restoration. Many forms of noise such as motion blur, camera misfocus etc., increases the complexity to restore the image. Image corruption comes in varying degrees of severity, the complexity of restoring photos in real-world applications will likewise vary greatly. Also, manual restoration is time consuming leading to lots of work to be piled up. To increase the capability of restoring old images from various defects, we must address several degradations intermingled in one old photo, such as structural defects like scratches and dust spots, and unstructured defects like sounds and blurriness. Furthermore, we may use a different face refinement network to restore small details of faces in ancient pictures, resulting in higher-quality photos. The aim of the work is to create a image restoration system that will be used to restore the images irrespective of the type of noise. In this paper, we present a model that would take image as an input and remove all the noises present in it to give a clean and restored image.
Keywords: Camera Misfocus, Image Restoration, Motion Blur, Noise, Restored Images.
Scope of the Article: Deep Learning