Rabbit Anti-Human TRP2 (a.a. 1-350) Polyclonal antibody

A new multi-image encryption scheme based on quaternion discrete fractional Hartley transform (QDFrHT) and an improved pixel adaptive diffusion is proposed, which can simultaneously increase the encryption capacity and reduce the consumption of keys. In the proposed scheme, the QDFrHT is proposed to generalize the discrete fractional Hartley transform to quaternion transform domain and then applied to multi-image encryption. The original images are compressed into four fusion images by discrete cosine transform (DCT) and Zigzag operations and then the resulting four images are represented as quaternion algebra. Afterward, the quaternion signal is processed with the proposed QDFrHT and the double random phase encoding technique. To enhance the security of the algorithm, the plaintext-related pixel adaptive diffusion and the pixel scrambling operation based on chaotic systems are followed to obtain the final encryption image. Different from the traditional cryptosystem whose secret keys are dependent on the plaintext, the designed cryptosystem ensures the sensitivity of the cryptosystem to plaintext while the selection of the secret keys is independent on plaintext by introducing an improved diffusion mechanism. Numerical experiments verify the feasibility and the efficiency of the proposed multi-image compression and encryption scheme. (C) 2020 Elsevier B.V. All rights reserved.

We present a new local window based image processing framework, which is particularly effective on edge-preserving and texture-removing. This seemingly contradictive effect is achieved by combining the traditional full window filtering strategy (FWF), which is good at removing noise, and the recently proposed side window filtering (SWF) strategy, which is good at preserving edges, so the new framework is called combined window filtering (CWF). By using window inherent variation method, we can easily distinguish the edges of structures from the texture. For the pixels on edges, SWF is used to preserve them and for the pixels on texture, FWF with multiple scales is used to remove them. This technique is surprisingly simple yet very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under CWF framework. Extensive analysis and experiments show that implementing the CWF principle can significantly improve their edge-preserving and texture-removing capabilities and achieve state of the art performances in applications.