Natural Image Deblurring using Recursive Deep Convolutional Neural Network (RDbCNN) and SecondGeneration Wavelets Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of secondgeneration wavelet transform is applied as preprocessing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (RDbCNN) is implemented to suppress any image blur affected by secondgeneration wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.

A Coarsetofine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2D kernel size estimation to a 1D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarsetofine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.