Volume 11 - Volume 11
Deblurring of Images using Novel Deconvolutional Neural Network (DNN) Algorithm to Enhance the Accuracy and Comparing with Richardson-Lucy Deconvolution Algorithm (RLD)
Abstract
Aim- Machine learning techniques are rapidly used in the area of digital image processing research
due to its impressive results in deconvolution and deblurring of images. The objective of this study is
to evaluate the performance of Novel DNN algorithm in deblurring of images by comparing it with
the RLD algorithm. Materials and Methods - Novel Deconvolutional Neural Network (DNN) and
Richardson-Lucy Deconvolution (RLD) algorithms were implemented to deblur the input images upto
256 pixels range. These algorithms were implemented to enhance the accuracy rate of deblurred
images using MATLAB Software and analyzed by collecting the dataset of 40 samples with 80% of
pretest power. Results - From the MATLAB simulation result, DNN achieves image deblurring rate
with 98% accuracy and RLD method achieves image deblurring rate with 92% accuracy. The
significance value obtained as (P < 0.002). Conclusion - Novel DNN classifier appears to have
better accuracy compared to RLD Classifier.
Paper Details
PaperID: 2178
Author's Name: K. Sai Dinesh and G. Uganya
Volume: Volume 11
Issues: Volume 11
Keywords: Digital Image Processing, Blur Classification, Point Spread Function, Deblurring, Novel DNN Algorithm, RLD Algorithm.
Year: 2021
Month: June
Pages: 1188-1200