i-manager Publications

Blur Image Detection and Classification using Resnet-50

Bhuvaneswari Polavarapu*, Hema Mamidipaka**

*-** Department of Electronics and Communication Engineering, JNTU-GV College of Engineering, Vizianagaram, Dwarapudi, Andhra Pradesh, India.

Periodicity:April - June'2022
DOI : https://doi.org/10.26634/jip.9.2.18875

Abstract

Blur classification is important for blind image restoration. It is difficult to detect blur in a single image without knowing any additional information. This paper uses edge detection methods and a deep learning convolutional neural network called Resnet-50 to classify blurry-type images. The Resnet model effectively reduces the gradient vanishing problem and uses connection skipping to train the network. Typically, images are subject to defocus blur and motion blur, which are caused by the incorrect depth of field and the movement of objects during capture. The dataset used here is the blur dataset from Kaggle, which consists of sharp images, images with blur, and motion blur. In this paper, edge detection methods are applied to images using Laplace, Sobel, Prewitt, and Roberts filters and derived features such as mean, variance, and maximum signal-to-noise ratio, which are used to train a classification algorithm for image classification.

Keywords

Blind Image Restoration, Convolutional Neural Network, Edge Detection Techniques, Resnet-50, SNR-Signal to Noise Ratio.

How to Cite this Article?

Polavarapu, B., and Mamidipaka, H. (2022). Blur Image Detection and Classification using Resnet-50. i-manager’s Journal on Image Processing, 9(2), 37-43. https://doi.org/10.26634/jip.9.2.18875

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