Abstract
Noise present in an image is an active challenge in the field of Digital Image Processing. Most computer vision applications such as object detection and edge detection are heavily influenced by the presence of noise. The presence of noise mandates pre-processing. Determining the noise level (whether high or low) is a demanding field in current times. Salt and pepper noise is the kind of noise that is commonly caused by dirt debris at the capturing tool and appears as black and white dots in an image. This work proposes a model to identify the level of salt and pepper noise in an image using a deep convolutional neural network (CNN or ConvNet). Once the noise levels are calculated, an appropriate de-noising filter can be applied. The proposed model achieves 98 percent of classification accuracy for salt and pepper quantification task using a data-set proposed in this work, which contains images having a different level of salt and pepper noise.
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Kumain, S.C., Kumar, K. Quantifying Salt and Pepper Noise Using Deep Convolutional Neural Network. J. Inst. Eng. India Ser. B 103, 1293–1303 (2022). https://doi.org/10.1007/s40031-022-00729-3
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DOI: https://doi.org/10.1007/s40031-022-00729-3