Search

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image. [ABSTRACT FROM AUTHOR]

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD. [ABSTRACT FROM AUTHOR]

Rapid advances in digital technology have facilitated us to transfer a huge amount of electronic files over the internet. But in the presence of malicious attackers, the security, as well as the integrity of such important files, becomes of utmost importance. Steganography, an art of hiding the data, ensures the security of these files over the internet, and this method has been used for a long. In this paper, our purpose is two-fold. First, the Ballot transform (BaT) produces an integer polynomial sequence in coefficient form for each non-overlapping m-pixel groups (m = 2 or 3) of the cover image. Second, the Genetic Algorithm (GA) is applied to generate a k-digit password using a method called Index Value Mapping (IVM) which decides positions of the transformed coefficients where the bits of the secret data can be embedded. The key idea of using GA here is to produce an optimal output in terms of stego image quality instead of using an exhaustive search considering all combinations of k number of bits. Experimental results show that the proposed method not only offers acceptable Peak Signal-to-Noise Ratio (PSNR) values with considerable payload but also provides two-way security of the data being transmitted over the digital medium. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning-based Steganography method for hiding secret information within the cover image. For this, we use a convolutional neural network (CNN) with Deep Supervision based edge detector, which can retain more edge pixels over conventional edge detection algorithms. Initially, the cover image is pre-processed by masking the last 5-bits of each pixel. The said edge detector model is then applied to obtain a gray-scale edge map. To get the prominent edge information, the gray-scale edge map is converted into a binary version using both global and adaptive binarization schemes. The purpose of using different binarization techniques is to prove the less sensitive nature of the edge detection method to the thresholding approaches. Our rule for embedding secret bits within the cover image is as follows: more bits into the edge pixels while fewer bits into the non-edge pixels. Experimental outcomes on various standard images confirm that compared to state-of-the-art methods, the proposed method achieves a higher payload. [ABSTRACT FROM AUTHOR]

Transform domain steganography methods are always preferable than spatial domain methods to the research community due to following advantages: better feature identification, improved security and higher robustness. In this paper, an integer sequence named Catalan Transform (CT) has been exploited in the image steganography domain. At the outset, the cover image is decomposed into 2 × 2 non-overlapping blocks in row major order. Then, each such block i.e., 4-pixel group is converted into transform domain using CT. Secret bits are embedded into the transformed components in varying proportions which facilitates us to achieve a payload in the range of 1 to 4 bpp. Inverse Catalan Transform (ICT) is applied over transformed cum embedded quadruples to generate the stego-pixels in spatial domain. Successive embedding operation over an entire image ensures the formation of stego-image. Experimental results confirm that the Peak Signal to Noise Ratio (PSNR) values obtained by the proposed method are always above the acceptable level (i.e., 30 dB) and at the same time, it outperforms many state-of-the-art methods. Other metrics such as Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Normalized Cross Correlation (NCC) and Global histograms with similarity measure have also been computed which prove the effectiveness of the proposed scheme. The robustness of the stego-images has further been tested using the StegExpose tool and it is found that the tool is unable to detect the presence of secret data. The code of this work is available at: https://github.com/Souradeep150/Catalan-based-Steganography. [ABSTRACT FROM AUTHOR]