Recent advancements in digital technology, data analytics, artificial intelligence, and medical research are fundamentally transforming healthcare delivery. The emergence of context-aware smart healthcare (SHC) has significantly influenced diverse domains such as medical informatics, communication systems, electronics, bioengineering, and healthcare ethics. By integrating modern biotechnologies with cutting-edge digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, edge and cloud computing, wearable medical devices, and blockchain, SHC systems are redefining traditional models to achieve greater efficiency, enhanced accessibility, and patient-centric care. This paper presents a comprehensive review of technological innovations driving smart rehabilitation, multidimensional impact of SHC technologies on modern rehabilitation practices, emphasizing their pivotal role in personalized treatment plans, real-time monitoring, and patient engagement for both physical and mental well-being. The current paradigm shifts in healthcare promises a positive transformation in the next few years, making it more personalized, and efficient healthcare experience for individuals facing various healthcare issues.

Major Depressive Disorder (MDD) is a debilitating mental health condition characterized by persistent sadness, anhedonia, and cognitive impairments that significantly disrupt daily functioning. Accurate diagnosis remains difficult due to the subjective nature of clinical assessments, highlighting the need for objective and automated diagnostic tools. Hence, this study proposes a novel multimodal framework integrating electroencephalography (EEG) and audio signals for accurate MDD detection. EEG signals undergo preprocessing and are transformed into 2D time-frequency (T-F) representations using the Superlet Transform, while audio signals are converted into Mel-spectrograms. The 2D representations from each modality are independently fed into a novel Vision Transformer (ViT) architecture. The proposed ViT first slices the T-F representation along frequency bands and applies positional encoding to each slice. The resulting slice embeddings are subsequently processed through a parallel Transformer Encoder (PE) module to effectively capture temporal dependencies. After the PE module has extracted sufficient information from the embedded slices, a learnable class token is appended to them, and the combined representation is passed through the Class Encoder (CE) module, allowing the model to capture global contextual information. Features extracted independently from EEG and audio streams are then fused and fed into a fully connected layer for final classification. Evaluation on the MODMA clinical dataset shows the framework achieves 98.86% accuracy, 98.32% F1-score, and 0.9403 MCC, surpassing unimodal baselines. The lightweight feature extraction and transformer-based fusion mechanisms enable the proposed architecture deployable in an edge-fog-cloud Internet of Medical Things (IoMT) system, resulting in low-latency, resource-efficient, and scalable remote diagnosis, enhancing accessibility and real-time clinical decision-making.

It is inevitable for dark images to have crucial information obscured by low-light conditions, which are worsened by the presence of noise in these images. This work introduces a groundbreaking solution, Noise-Suppressing Dark Image Enhancement for Web Apps (NSDIE), to address the challenging task of enhancing low-light images marred by noise. The proposed work utilizes a low-rank model with simultaneous enhancement of reflectance and illumination components to improve the nighttime scenes while also eradicating the present noise of the image. The reflectance component is further processed using a multiscale retinex model to compensate for the possible color distortions while the illumination component is enhanced using the camera response model to ensure the genuineness of the scene. The proposed work is also tested for a standalone application and is presented to the user through a web portal to aid the concerns of dark image enhancement in the daily life of the user. Rigorous quantitative and qualitative analyses assert NSDIE's superiority over existing techniques, establishing its pivotal role in addressing the critical concern of dark image enhancement.

This article presents CHBS, a novel context-based histogram bin stretching method that enhances the contrast by increasing the range of gray levels and randomness among the gray levels. It comprises image spatial contextual information and discrete cosine transform (DCT). It constitutes the global enhancement with the context-based histogram bin stretching and local details with the DCT. First, it uses the spatial similarities among surrounding pixels to generate random numbers. Unlike the other methods, the similarity map is generated based on the neighboring pixels’ mutual relationship. Intensity values are distributed among the available dynamic range to generate a global contrast-enhanced image. Second, the DCT is further applied to the previous contrast-enhanced image to adjust its local details automatically. Several experiments are conducted on the different levels of contrast degraded images. Both subjective and objective assessment outcomes validate that the projected approach is better or comparable with several state-of-the-art approaches in terms of brightness preservation, richer details, and natural appearance.

Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.

In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image's spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme's qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.

While yielding satisfactory segmentation results for images with low SNR and poor contrast, one-dimensional (1-D) and two-dimensional (2-D) Otsu's thresholding methods have the downside of high computational complexity. So far, three-dimensional (3-D) Otsu method has been based on histogram, which has only probability distribution of pixels as an object of interest. Histogram-based segmentation methods do not consider the contextual information which is significant to enrich the quality of segmented image. In this paper, a context-based 3-D Otsu algorithm has been proposed that considers the pixel intensity values as well as spatial information along with same properties of histogram. The proposed method is evaluated comprehensively with respect to quality and a detailed analysis is presented to compare the results of histogram-based 1-D, 2-D, and 3-D Otsu and energy-based 1-D, 2-D, and 3-D Otsu method, respectively. Experimental outcomes demonstrate the superiority of energy-based 3-D Otsu algorithm compared to histogram-based methods in terms of improved performance metrics, including mean error (ME), mean square error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), and entropy. Experiments on standard daily life color images have been carried out to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.

In this study, a new enhancement framework is proposed for low contrast and dark images where traditional histogram equalisation (HE), gamma and logarithmic transformation are incorporated to achieve a visually pleasing image. Before the operation of HE on the input image, gamma and logarithmic transformation are performed in order to preserve the fine details of the image. A new gamma value of the proposed algorithm helps to restrain histogram spikes to avoid over‐enhancement and noise artefacts effect. After that, a novel logarithmic transformation is used to map a narrow range of low‐intensity values in the input image to a wider range of output levels. Thus, the dark input values are spread out into the higher intensity values, which improve the overall contrast and brightness of the image. The proposed method is compared with various state‐of‐the‐art techniques. The large dataset has been used to check the feasibility of the technique. The subjective and objective analysis shows that the proposed algorithm outperforms most of the existing contrast‐enhancement algorithms and the results are natural‐looking, good contrast images with almost no artefacts.

Low light image enhancement algorithms intent to produce visually pleasant images and target to extract valuable information for computer vision applications. The task of improving the quality of low light images is a challenging one. The existing methods for quality improvement undeniably annoy the visual aesthetics and suffer the major drawback of high computational complexity and less efficiency. To improve the visual quality and lower the distortions, a simple and computationally efficient low light image enhancement framework is presented in this study. To achieve this, an adaptive sigmoid transfer function (ASTF) is used and is derived from the sigmoid activation function of neural networks. By combining ASTF with a Laplacian operator, colour and contrast‐enhanced images are obtained. Experiments show the effectiveness of the proposed method with state‐of‐the‐art methods.

This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c-means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images.

A novel white balancing algorithm is proposed in this paper to automatically enhance the global contrast degraded imperceptible images. The technique is applied on four publicly available image dataset, CSIQ, KADID, TID and SIPI. Colour images consist of three channels viz. Red, Blue and Green. A contrast degraded colour image visually appears similar to an image with one or more distorted channel. 12 images are obtained by enhancing one channel of the contrast degraded image at the cost of other channel using White Balancing algorithm. Four images with best quantitative performance metrics, visual similarity index (VSI), gradient magnitude similarity index (GMSD), patch-based contrast quality index (PCQI) and peak signal-to-noise ratio (PSNR) determines the pair of weak and prominent channels. An optimization algorithm then enhances these channels and the image with the best quantitative performance metrics is chosen as the enhanced image. Quantitative and qualitative results demonstrate that the proposed method produces an enhanced image with superior perceptual quality, and gives the best average results for all the parameters across every dataset as compared to the state-of-the-art methods.

Histogram equalisation (HE) is a simple and effective image enhancement technique. However, it suffers from excessive brightness change and provides degradation in the visual aspect of the image. To overcome the shortcomings in the HE, a novel histogram framework is proposed in this study. The image histogram is first segmented into two parts using the Otsu's thresholding method. Then, both of the upper and lower histograms are constrained to control the level of enhancement. These constraint parameters are computed through moth‐flame optimisation algorithm. After constraining the histograms, mean shift correction is performed to ensure there is a minimum level of mean shifting from input to output image. Traditional HE is then applied with a modified histogram to obtain mapping function for lower and upper grey level individually. This enhanced image provides a balance between the level of enhancement and preservation of the important features of the image for high‐level processing. The effectiveness of the proposed method is highlighted with a detailed comparison with other closely related schemes. Through the proposed routine, the enhanced images achieve a good trade‐off between features enhancement, low contrast boosting, and brightness preservation in addition to the natural feel of the original image.

In this study, an image enhancement algorithm based on the modified histogram clipping scheme using a difference of histogram bins (MCDHB) has been proposed. The core idea of the proposed method is to ascertain the difference between the number of pixels’ in histogram bins of an input image and that of the traditional histogram equalised (HE) image. The calculated difference of each bin is partitioned into different blocks based on range criteria. The proposed algorithm can be attested as a global HE approach and mainly focuses on maintaining peaks in the histogram. The proposed MCDHB framework provides a good trade‐off among contrast enhancement, shape of histogram, detailed information, and natural colour. Furthermore, the MCDHB framework is also incorporated with gamma correction for further improvement. The subjective and objective assessment confirms that both the proposed techniques can efficiently enhance the images, in a better way than those produced by classical techniques.

In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.

In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm‐based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal‐to‐noise ratio (PSNR) and signal‐to‐noise ratio (SNR) as compared to PSO‐based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state‐of‐the‐art image denoising techniques.

In this study, an improved multi-band satellite contrast enhancement technique based on the singular value decomposition (SVD) and discrete cosine transform (DCT) has been proposed for the feature extraction of low-contrast satellite images using normalised difference vegetation index (NDVI) technique. The method employs multi-spectral remote sensing data technique to find the spectral signature of different objects such as the vegetation index and land cover classification presented in the satellite image. The proposed technique converts the image into the SVD–DCT domain and after normalising the singular value matrix; the enhanced image is reconstructed by using inverse DCT. The visual and quantitative results included in this study clearly show the increased efficiency and flexibility of the proposed method over the existing methods. The simulation results show that the enhancement-based NDVI using DCT–SVD technique is highly useful to detect the surface features of the visible area which are extremely beneficial for municipal planning and management.