Efficient Motion Blur Detection and Removal Using Deep CNN: A Synthetic Dataset Approach

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 the surge in fields like self-driving cars, UAVs, and medical image processing. Due to low light conditions and the camera's fast motion, a large portion of image data generated is wasted. Such motion-blurred images present a major challenge to 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 for training, reconstructed image quality, etc. The proposed paper utilizes a learning method to detect and deblur single input images even without a ground-truth sharp image. We have employed a synthetic dataset for experimental evaluation. This synthetic dataset, created and used for training the DCNN model, has been made available open-source on Kaggle at the following link: https://www.kaggle.com/dikshaadke/motionblurdataset

The proposed method employs a deep convolutional neural network (DCNN) to learn the motion blur pattern from the input image. The DCNN comprises multiple convolutional layers followed by pooling layers and fully connected layers. The network is trained using a large synthetic dataset of motion-blurred images and their corresponding sharp images. The synthetic dataset is generated by applying various motion blur kernels to high-quality images.

During the testing phase, the input image is fed into the trained DCNN, which predicts the motion blur pattern and generates a deblurred image. The proposed method achieves state-of-the-art results in terms of both motion blur detection and removal. Additionally, the method is computationally efficient and can process high-resolution images in real-time.

In conclusion, the proposed method offers an effective solution for motion blur detection and removal in computer vision applications. The method can be extended to other types of blur, such as defocus blur and camera shake. The availability of the synthetic dataset used in this paper provides a valuable resource for researchers to develop and evaluate new methods for motion blur detection and removal.