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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]

• Laguerre transform performs integer based calculation and therefore the operations are faster. • The binary factorization of the transform matrix enables efficient hardware implementation. • The computational complexity of the Laguerre transform is O (n × log(n)). • Proposed method supports widely used image formats such as BMP, PPM, PGM, and TIFF. • Variable payload with considerable stego-image's quality distortion is achieved. • StegExpose tool ensures that the robustness of the stego-images is very high. Steganography, an approach used to conceal information into the digital media, generally works in two domains: spatial and transform. Though spatial domain methods are simpler, but transform domain methods are good at identifying the features which make the end system more secure. In this work, a novel Steganographic scheme based on an integer sequence named Laguerre transform (LT) is proposed. The Cover image is decomposed into non-overlapping m-pixel groups and then each such pixel group is transformed by applying LT. Variable length bits from the secret information are fabricated into the transformed components. A post-embedding adjustment is applied over these components to minimize the distortion. By applying Inverse LT (ILT), the m-pixel groups are re-computed from the resulting adjusted components. Experimental results reveal that disparity between cover and stego-pixels increases as m increases. Proposed scheme offers better stego image and higher payload compared to some state-of-the-art techniques. Code of this method is publicly available here. Image, graphical abstract [ABSTRACT FROM AUTHOR]