Name
Title | ||
|---|---|---|
A novel diagnostic information based framework for super-resolution of retinal fundus images. |
Abstract | ||
|---|---|---|
•To the best of our knowledge, the problem of resolution enhancement of the retinal images has not been addressed.•The proposed method takes into consideration the diagnostic information in the retinal fundus image during super-resolution (SR).•The method performs SR only on the zone of interest rather than the entire image. This leads to computational time efficiency.•A set of novel fundus image specific features are extracted to classify the diagnostically significant and non-significant zones.•A trade-off between the learning based method and bicubic interpolation is performed during SR which leads to achievement in computational time without loss in reconstruction accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compmedimag.2019.01.002 | Computerized Medical Imaging and Graphics |
Keywords | Field | DocType |
Fundus image,Discrete wavelet transform,Contrast sensitive function,Shannon entropy,Support vector machine (SVM),Sparse representation,Bicubic interpolation,Super-resolution | Computer vision,Peak signal-to-noise ratio,Support vector machine,Bicubic interpolation,Fundus (eye),Artificial intelligence,Retinal,Classifier (linguistics),Medicine,Entropy (information theory),Calibration | Journal |
Volume | ISSN | Citations |
72 | 0895-6111 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vineeta Das | 1 | 5 | 2.56 |
| S. Dandapat | 2 | 261 | 28.51 |
| Prabin Kumar Bora | 3 | 5 | 2.22 |