Name
Abstract | ||
|---|---|---|
We propose a radiance domain denoising frame work for the high dynamic range (HDR) imaging problem. The proposed method uses a maximum aposteriori probability (MAP) based reconstruction of the HDR image with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided. Our method yields better results, with the edges well preserved and noise reduced considerably. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6115682 | ICIP |
Keywords | Field | DocType |
image denoising,image reconstruction,maximum likelihood estimation,probability,HDR image recobstruction,camera response function,high dynamic range imaging problem,image domain denoising,majorize-minimize method,maximum aposteriori probability,noisy observations,quadratic function,radiance domain denoising framework,total variation | Iterative reconstruction,Noise reduction,Computer vision,Noise measurement,Computer science,Smoothing,Quadratic function,Artificial intelligence,High dynamic range,High-dynamic-range imaging,Radiance | Conference |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.37 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renu M. Rameshan | 1 | 5 | 4.84 |
| Subhasis Chaudhuri | 2 | 1384 | 133.18 |
| Rajbabu Velmurugan | 3 | 61 | 11.64 |