Sure! Here are some papers related to Digital Image Processing:
Li, X., & Wu, X. (2018). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 39(25), 9184-9201.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6), 610-621.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence.
Singh R.K.(2020) Comparative Study Of Various Edge Detection Techniques In Image Processing using MATLAB International Journal of Innovative Technology and Exploring Engineering Vol-9 Issue-2
These papers cover a range of topics within digital image processing such as image classification techniques using machine learning algorithms like deep residual networks or edge detection methods using different filters in the spatial domain or frequency domain.
Please let me know if you need more information about any specific topic within digital image processing!
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