Anna Vidal

Rank-Ordered differences statistic based switching vector filter

In this paper a new switching vector filter for impulsive noise removal in color images is proposed. The new method is based on a recently introduced impulse detector named Rank-Ordered Differences Statistic (ROD) which is adapted to be used in color images. The switching scheme between the identity operation and the Arithmetic Mean Filter is defined using the mentioned impulse detector. Extensive experiments show that the proposed technique is simple, easy to implement and significantly outperforms the classical vector filters presenting a competitive performance respect to recent impulsive noise vector filters.

Image Noise Removal on Heterogeneous CPU-GPU Configurations.

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.