Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, realworld paired datasets. To bridge this gap, we introduce the Low-Light Smartphone Dataset (LSD), a large-scale, high-resolution (4K+) dataset collected in the wild across a wide range of challenging lighting conditions (0.1–200 lux). LSD contains 6,425 precisely aligned low and normallight image pairs, selected from over 8,000 dynamic indoor and outdoor scenes through multi-frame acquisition and expert evaluation. To evaluate generalization and aesthetic quality, we collect 2,117 unpaired low-light images from previously unseen devices. To fully exploit LSD, we propose TFFormer, a hybrid model that encodes luminance and chrominance (LC) separately to reduce color-structure entanglement. We further propose a cross-attention-driven joint decoder for context-aware fusion of LC representations, along with LC refinement and LC-guided supervision to significantly enhance perceptual fidelity and structural consistency. TFFormer achieves state-of-the-art results on LSD (+2.45 dB PSNR) and substantially improves downstream vision tasks, such as low-light object detection (+6.80 mAP on ExDark).

The secrecy and security of patients' details are among the biggest concerns in Healthcare Information Systems. The Electronic Patient Records (EPR) data, along with doctors' comments can be embedded inside carrier DICOM Images using the proposed scheme. The confidential information is scattered into different sets, and rather than embedding it in a single DICOM image, it is embedded into multiple carriers for enhanced security. These scans can be used together to hide the confidential patient data using the proposed technique. The prototype steganography scheme is tested utilizing LSB Substitution and dummy secret data is embedded inside DICOM Images. The achieved results are imperceptible to the human visual system (HVS). Performance matrices i.e., PSNR, MSE, RMSE, SSIM, NR-IQA parameters (BRISQUE, NIQE, PIQE), as well as entropy, are calculated for cover and stego images. The proposed scheme has been found to be resilient and computationally secure.

ABSTRACT In the context of Cloud and Fog computing settings, recent developments in deep learning techniques show great potential for changing several fields, including healthcare. In this study, we make a contribution to this changing field by proposing an enhanced deep learning‐based strategy for classifying chest X‐ray images, using pre‐trained models such as RetinaNet, EfficientNet and Faster‐R‐CNN, which we use through transfer learning. Our strategy outperforms single models and traditional techniques by leveraging critical data gleaned from multiple models, demonstrating the ability of deep learning to improve diagnostic precision. Our approach presents a novel dual‐check system in the context of worries about security, privacy and trust in Cloud and Fog‐based Smart Systems. In this case, a decision support system uses chest X‐ray images to make an initial diagnosis that is then confirmed by a medical practitioner. This cooperative strategy not only reduces diagnostic errors that come from machine and human sources but also emphasises how crucial it is to incorporate AI‐driven solutions into safe and reliable healthcare ecosystems. Our approach raises the bar for the quality of patient care and healthcare outcomes by overcoming the drawbacks of traditional diagnostic methods that depend on the subjective opinions of physicians. Our work brings out how deep learning might transform clinical diagnostics by distinguishing inflammatory regions in chest X‐ray images. Research is needed to fully grasp the transformative potential of deep learning in medical image processing, especially as the healthcare industry continues to embrace AI‐driven solutions. Further research endeavours have to dig into tactics like broadening the scope of datasets, executing data augmentation methodologies and incorporating bespoken features to augment the elasticity and effectiveness of AI‐driven diagnostic systems.

Apart from high-level computer vision tasks, deep learning has also made significant progress in low-level tasks, including single image dehazing. A well-detailed image looks realistic and natural with its clear edges and balanced colour. To achieve a clearer and vivid view, we exploit the role of edges and colours as a significant part of our proposed work. A progressive two-stage image dehazing network is presented to overcome the challenges of current image dehazing algorithms. The proposed image dehazing framework is divided into two steps; in the first stage, the multiscale image features of the encoder and decoder structure can be extracted. The second stage consists of the Color Correction Model (CCM), which retrieves balanced colour close to the ground truth. The encode-decoder network consists of a dense residual attention unit (DRAU) that comprises channel attention with pixel attention mechanisms. We have seen that weighted information and the haze difference is inconsistent across pixels without DRAU at the various channel-specific features. DRAU deals with different features and pixels unequally, which offers more versatility in handling knowledge of various types of detailed information. Our proposed two-stage network exceeds state-of-the-art algorithms in both visual and quantitative aspects. The findings are tested with the best-published peak signal-to-noise ratio metrics of 33.55–33.44 dB and SSIM 0.9619–0.9714 on SOTS indoor and outdoor test data sets.

Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to-end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks' performance and reveal the potentiality of such an enhancement method in real-world applications.