This paper is published in Volume-6, Issue-6, 2020
Area
Image Processing
Author
Pankaj Tanwar, Karishma Kumari, Saqib Kamal
Org/Univ
Samsung Research and Development Institute, Noida, Uttar Pradesh, India
Pub. Date
27 November, 2020
Paper ID
V6I6-1161
Publisher
Keywords
Image Segmentation, Visual Distractor, Salient Regions, Visual Attention, Image Enhancement, Random Forest

Citationsacebook

IEEE
Pankaj Tanwar, Karishma Kumari, Saqib Kamal. Smart image enhancement technique by removal of undesirable objects/background from the image, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Pankaj Tanwar, Karishma Kumari, Saqib Kamal (2020). Smart image enhancement technique by removal of undesirable objects/background from the image. International Journal of Advance Research, Ideas and Innovations in Technology, 6(6) www.IJARIIT.com.

MLA
Pankaj Tanwar, Karishma Kumari, Saqib Kamal. "Smart image enhancement technique by removal of undesirable objects/background from the image." International Journal of Advance Research, Ideas and Innovations in Technology 6.6 (2020). www.IJARIIT.com.

Abstract

The devil is always in your picture. You always miss the best shot and that particular tiny object in your photo can worsen the entire composition and result in something opposite from what you hoped for. The proposed AI based image enhancement model will first detect unwanted objects/background based on the training of large set of already edited images by users then intelligently reconstructs the image without those objects. We have prepared a dataset with distracting elements in the images and used it to train our predictor model which predicts the distracting regions and thereafter used image-inpainting to remove those areas which results in a standalone system for distractor removal with no user input. In the proposed method of this paper, the image is first segmented using Convolutional networks for semantic segmentations and then each segment is classified in terms of the score of distractors on the basis of various features which almost covers all types of distractors in an image. Our main focus was to collect the data which contains all kinds of distractors and then deciding the features which classify an object as a distractor in an image. Detection and removal of distracting regions helps to enhance the beauty and visual quality of the image which can be fulfilled by using our model.
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