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

The proposed method uses a deep convolutional neural network (DCNN) to learn the motion blur pattern from the input image. The DCNN consists of 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 to 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. Furthermore, the method is computationally efficient and can process high-resolution images in real-time.

In conclusion, the proposed method provides 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