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A new fingerprint enhancement algorithm is presented. The technique consists of two anisotropic diffusion schemes. One is the coherence-enhancing diffusion. Another is the modified edge-enhancing diffusion. At first, the degraded fingerprint image is processed by the coherence-enhancing diffusion to obtain the initial processed image. And then, the modified edge-enhancing diffusion operates on the initial processed image to obtain the final enhancement image. The tests show the proposed new method gets the better performance obviously compared to the original coherence-enhancing diffusion-based method.

In this paper, a modification to belief propagation (BP) decoding algorithm is proposed, which is based on extracting the prior messages of each variable node to help the BP decoding, and is particularly effective for low-density parity-check (LDPC) codes with short cycles, where the existence of cycles makes the original BP algorithm perform suboptimal. The proposed algorithm, referred to as "employing the positive effects of the feedback messages (EPEFM)", extracts the positive effects of the feedback messages and then makes use of them as prior messages to assist the decoding of the BP algorithm. Simulation results confirm the effectiveness of our proposed algorithm, which improves the performance in high signal-to-noise-ratio (SNR) region without loss in low SNR region.