Detection and Blur-Removal of Single Motion Blurred Image using Deep Convolutional Neural Network
Diksha Adke, Atharva Karnik, Honey Berman, Shyamala Mathi · 2021
This paper proposes a simple and efficient motion blur detection and removal method based on Deep CNN. The domain of computer vision has gained significant importance in recent years due to insurgence in the fields of self-driving cars, UAVs, medical image processing, etc. Due to low light conditions and the camera’s fast motion, a large portion of image data generated is wasted. Such motion-blurred images impose a great challenge to the algorithms used for decision-making in machine vision. Although there have been significant improvements in denoising such image data, these methods are challenged by time constraints, insufficient data to train, reconstructed image quality, etc. The proposed paper employs a learning method to detect and deblur the single input image even in the absence of a ground-truth sharp image. We have used a synthetic dataset for experimental evaluation. This synthetic dataset that we have created and used for training the DCNN model has been made available for open source on Kaggle at the following link: https://www.kaggle.com/dikshaadke/motionblurdataset