A General Regression Neural Network based Blurred Image Restoration

Image distortion may result from a variety of factors, such as changes in electronic imaging equipment that create noise. The goal of blur image alignment is to determine which images are blurred and to restore them. Due to this, a unique blurred image restoration approach is developed, which consists of a point spread function, canny edge detection, GLCM extraction, and general regression neural network for identifying the type of blurred images, and a wiener filter for restoring the image. This technique uses a combination of the General Regression Neural Network (GRNN) and Deep Neural Network (DNN) to detect the kind of a blur and determine the calibration efficiency of the DNN and the degradation efficiency of the GRNN. The mean square error (MSE), the covariance factor, and the peak signal-to-noise ratio (PSNR) are used to assess the effectiveness of the proposed method. In comparison to conventional procedures, the proposed method yields superior outcomes.