Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

ABSTRACT

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.

KEYWORDS:

• Digital image processing
• artificial neural network
• Pillar K-mean filtering
• collaborative filtering
• image inpainting
• Mat Lab
• Peak Signal to noise ratio (PSNR)
• mean Signal Error (MSE)
• signal to noise ratio (SNR)
• Davies–Bouldin Index (DBI)
• Dunn Index (DI)
• Silhouette Index (SI)

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Dr Kanika Gupta is working as an Associate Professor in Information Technology Department in ABES Engineering College, Ghaziabad affiliated to AKTU Lucknow. She did her Bachelor’s degree with Hons. from UPTU in 2007 and Master of Technology in 2011 from IP University, Delhi. She did her Ph.D. from Noida International University in 2017. She has published various papers in different journals and conferences. She is an active member of ACM professional society and faculty sponsor of ACM-W Chapter of ABES Engineering College. Her areas of interest are Digital Image Processing, Neural Networks, and Genetic Algorithms.

Ms Nandita Goyal is working as Senior Assistant Professor in Information Technology Department in ABES Engineering College, Ghaziabad affiliated to AKTU Lucknow. She did her Bachelor’s degree with Hons. from UPTU in 2005 and Master of Technology in 2007 from JIITU. She has published various papers in different journals and conferences. She is an active member of IEI professional society. Her areas of interest are Cloud Computing, Artificial Intelligence, Neural Networks and Machine Learning. She is pursuing her Ph.D. in Cloud Forensics from Dr. A.P.J. AKTU University, Lucknow.

Harsh Khatter is working as an Assistant Professor in ABES Engineering College, Ghaziabad affiliated to AKTU Lucknow. He did his Bachelor’s degree in Computer Science and Engineering in 2010 and Master of Technology in 2012. He has published eight research papers in IEEE conferences and journals. He is active member of IEEE and CSI Professional Society. His areas of interests are web mining and information retrieval, content curation in text, images and documents, Neural Network and fuzzy inference. He is pursuing his Ph.D. in Content Curation from Dr. A.P.J. AKTU University, Lucknow.