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Transform-based techniques partially address challenges like robustness and the imperceptibility in image steganography. Such approaches, however, increase the memory requirement and reduce the quality of the cover image and hiding capacity. Moreover, the steganography is always coupled with cryptography to strengthen the confidentiality. This paper presents an adaptive hybrid method for image steganography procedure based on bit reduction and pixel adjustment using the fuzzy logic and integer wavelet transform technique. The fuzzy set theory provides powerful tools to represent and process human knowledge in the form of fuzzy if-then rules that can resolve difficulties in image processing arising due to the uncertainty of the data, tasks, and results. We apply a bit reduction algorithm to each byte of the data which are to hide in the cover image. This decreases the memory usage and increases the capacity. The embedding of the input text into the cover image distorts the cover image. Hence, to minimize the visual difference between the cover image and the text embedded image, an optimum pixel adjustment algorithm is applied to the text embedded image. Simulation results demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concepts of stratified fuzzy soft topogenous, stratified fuzzy soft filter, stratified fuzzy soft quasi-proximity and stratified fuzzy soft grill. Also, we introduce the concept of fuzzy soft topogenous structures by combining fuzzy soft topogenous with fuzzy soft filter and we introduce the concept of fuzzy soft quasi-proximity by combining fuzzy soft quasi-proximity with fuzzy soft grill and give their properties. Furthermore, we establish the relationship among these fuzzy soft topological structures and their stratifications. [ABSTRACT FROM AUTHOR]

Impulse noise, also known as impulsive noise, is one of the most common types of noise occurring in digital images. The median filter and morphological filters are often used to remove impulsive noise, but these filters do not preserve image details. In this paper, we first apply the median filter in order to reduce the amount of impulsive noise in a corrupted image. After an application of the direct fuzzy transform (FT) to the resulting image, we restored the pixel values corresponding to locations flagged by the fuzzy rule-based noise detector by means of the inverse fuzzy transform. Finally, we obtained the output of our proposed image filter by combining the restored pixels with the ones marked as noiseless by the aforementioned noise fuzzy detector. We compared the results of our approach that we called adaptive FT-based image filter (AFT-IF) with the ones obtained by a number of other image filters for impulsive noise reduction. [ABSTRACT FROM AUTHOR]

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. [ABSTRACT FROM AUTHOR]