Underwater images degraded due to low contrast and visibility issues. Therefore, it is important to enhance the images and videos taken in the underwater environment before processing. Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images. The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available, low resolution, and blurriness in underwater images caused by the normal camera. Various researchers have proposed different solutions to overcome these problems. Dark channel prior (DCP) is one of the most used techniques which produced a better Peak Signal to Noise Ratio (PSNR) value. However, DCP has some issues such as it tends to darken images, reduce contrast, and produce halo effects. The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization (CLAHE) and the Adaptive Color Correction Method. The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and some images were collected from the internet. The measure of entropy (MOE), Measure of Enhancement (EME), Mean Square Error (MSE), and PSNR opted as performance measures during experiments. The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.

Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these vectors. Finally, the optimized features are serially fused to pass them to the classifier. A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013, MMA, and CK+ to report performance based on various metrics. Accuracy results show that the proposed model has achieved 82.5% accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy. We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.