Image processing research areas using GPU I'm an undergraduate student and I am currently trying to find suitable topic for my bachelor thesis. The broader topic I've selected is "Image processing on GPU", so basically I'll be developing image processing algorithms specifically for GPU.

The problem is, I need to do a novel approach, not reinvent a wheel.  There's so much going on in image processing, lately on GPUs as well, that I'm having trouble finding a suitable area that hasn't been researched in this way and which isn't above my capabilities as an undergraduate.

The question is, can you point me in a direction what are current research areas in image processing that could benefit from GPU implementation? I'd love to do something for medical or astrophysical applications, but I'm open to other areas as well.

Thanks!
I've done a bit of GPGPU (for optical flow) during my PhD, so here are a few hints to help you:


choose a computation intensive task. You're very likely to find them in variational inverse problems (especially when expressed images instead of sparse feature sets). They are very common tasks: image deconvolution, compressed sensing, dense optical flow (and more broadly dense image registration...), dense 3D reconstruction, image segmentation... ;
from my point of view, non-local image denoising (using non-local means) is very easy to understand, and it's computation-intensive enough to have been included in NVidia's Cuda examples for quite a while. It really deserves a new, better GPU implementation (such as the GPU-based kd-trees proposed a few years ago). However, it may prove challenging for an undergrad;
a useful site to see what Image Processing tasks can be done on GPUs: the Austrian (T.U. Graz) lab GPU4Vision.