Gamma correction is an interesting method for improving image quality in uncontrolled illumination conditions case. This paper presents a new technique called Mean-Variance Gamma (MV-Gamma), which is used for estimating automatically the amount of gamma correction, in the absence of any information about environmental light and imaging device. First, we valued every row and column of image pixels matrix as a random variable, where we can calculate a feature vector of means/variances of image rows and columns. After that, we applied a range of inverse gamma values on the input image, and we calculated the feature vector, for each inverse gamma value, to compare it with the target one defined from statistics of good-light images. The inverse gamma value which gave a minimum Euclidean distance between the image feature vector and the target one was selected. Experiments results, on various test images, confirmed the superiority of the proposed method compared with existing tested ones.

Two essential problems are still posed in terms of Radio Frequency Identification (RFID) systems, including: security and limitation of resources. Recently, Li et al.'s proposed a mutual authentication scheme for RFID systems in 2014, it is based on Quasi Cyclic-Moderate Density Parity Check (QC-MDPC) McEliece cryptosystem. This cryptosystem is designed to reducing the key sizes. In this paper, we found that this scheme does not provide untraceability and forward secrecy properties. Furthermore, we propose an improved version of this scheme to eliminate existing vulnerabilities of studied scheme. It is based on the QC-MDPC McEliece cryptosystem with padding the plaintext by a random bit-string. Our work also includes a security comparison between our improved scheme and different code-based RFID authentication schemes. We prove secrecy and mutual authentication properties by AVISPA (Automated Validation of Internet Security Protocols and Applications) tools. Concerning the performance, our scheme is suitable for low-cost tags with resource limitation.