Environmental factors such as fog and haze affect the image quality and make it unsuitable for automated systems, such as, intelligent vehicles, surveillance and outdoor object recognition, which require images with clear visibility for processing and decision making. In general, reconstruction of fog-free image from a single input image is quite challenging. Dark channel prior (DCP) method is used to estimate atmospheric light for the purpose of image defogging. This paper presents a DCP based image defogging method with improved transmission map to avoid blocking artifacts. The transmission maps are computed for RGB and YCbCr color spaces. Three transmission maps for the R, G, and B channels are utilized to compute a mean transmission map. In the YCbCr color space, Y channel is used to calculate the transmission map. The two transmission maps are refined by preserving edge information for constructing two intermediate images, which are assigned different weights to get the enhanced defogged output. The proposed method is evaluated against the current state-of-the-art approaches, and the experimental results based on structural similarity index, fog effect, anisotropic quality index and degradation score are calculated, which show that the defogged images reconstructed using the proposed method achieved better results with lower fog effect, similarity index, degradation score and higher quality index value. Reconstructed image has better contrast and luminance which is perceptually more appealing to the human visual system.

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.

In an era dominated by visual content, perceptual image quality assessment (IQA) is crucial for enhancing user experiences and driving technological advancements across various domains. This survey paper reviews the integration of Vision Transformers (ViTs) into both no-reference (NR) and full-reference (FR) IQA methods, highlighting their promise as alternatives to traditional techniques. ViTs leverage attention mechanisms to focus selectively on relevant image patches, showing promise in aligning more closely with human perceptual errors. We identify key limitations of conventional IQA methods and track the evolution from early learning-based approaches to contemporary deep learning models, with a specific focus on ViTs.We discuss the performance of Transformer-based models in capturing image distortions and their strong correlation with subjective IQA metrics. We also discuss potential breakthroughs, including the development of hybrid architectures combining Capsule Networks and Transformers, adaptive IQA through meta-learning, and scalable solutions using quantum-inspired computing. These advancements promise to enhance perceptual quality assessment, with substantial implications for industries such as medical imaging, multimedia applications, and beyond. This study aims to set the groundwork for future research in transformer-based methodologies, offering new insights into the transformative impact of these models on IQA.