In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images.[1] As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.


1. ResGAN: A Low-level Image Processing Network to Restore Original Quality of JPEG Compressed Images (IEEE 2019)

2. Detection of Liver Cancer using Image Processing Techniques(IEEE 2019)

3. A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images(IEEE 2019)

4. Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information(IEEE 2019)

5. Identification of Plant Disease using Image Processing Technique(IEEE 2019)

6. Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning(IEEE 2019)

7. DeepISP: Toward Learning an End-to-End Image Processing Pipeline(IEEE 2019)

8. Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality(IEEE 2019)

9. Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation(IEEE 2019)

10. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction(IEEE 2019)