Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.

One of the most common causes of traffic accidents is human error. One such factor involves the drowsy drivers that do not focus on the road before them. Driver drowsiness often occurs due to fatigue in long distances or long durations of driving. The signs of a drowsy driver may be detected based on one out of three types of tests; i.e., performance test, physiological test, and behavioural test. Since the physiological and performance tests are quite difficult and expensive to implement, the behavioural test is a good choice to use for detecting early drowsiness. Behaviour-based driver drowsiness detection has been one of the hot research topics in recent years and is still increasingly developing. There are many approaches for behavioural driver drowsiness detection, such as Neural Networks, Multi Layer Perceptron, Support Vector Machine, Vander Lugt Correlator, Haar Cascade, and Eye Aspect Ratio. Therefore, this study aims to conduct a systematic literature review to elaborate on the development and research trends regarding driver drowsiness detection. We hope to provide a good overview of the current state of research and offer the research potential in the future.

Image blur is one of the common degradations on an image. The blur that occurs on the captured images is sometimes non-uniform, with different levels of blur in different areas of the image. In recent years, most deblurring methods have been deep learning-based. These methods model deblurring as an imageto-image translation problem, treating images globally. This may result in poor performance when handling non-uniform blur in images. Therefore, in this paper, the author compared two state-of-the-art supervised deep learning methods for deblurring and restoration, e.g. BANet and Restormer, with a special focus on the non-uniform blur. The GOPRO training dataset, which is also used in various studies as a benchmark, was used to train the models. The trained models were then tested on the GOPRO testing test, the HIDE testing set for cross-dataset testing, and GOPRO-NU, which consists of specifically selected non-uniform blurred images from the GOPRO testing set, for the non-uniform deblur testing. On the GOPRO testing set, Restormer achieved an SSIM of 0.891 and PSNR of 27.66 while BANet obtained an SSIM of 0.926 and PSNR of 34.90. Meanwhile, for the HIDE dataset, Restormer achieved an SSIM of 0.907 and PSNR of 27.93 while BANet obtained an SSIM of 0.908 and PSNR of 34.52. Finally, on the non-uniform blur GOPRO dataset, Restormer achieved an SSIM of 0.911 and PSNR of 29.48 while BANet obtained an SSIM of 0.935 and PSNR of 35.47. Overall, BANet shows the best result in handling non-uniform blur with a significant improvement over Restormer.

Computer vision tasks often fail when applied to night images, because the models are usually trained using clear daytime images only. This creates the need to augment the data with more nighttime image for training to increase robustness. In this study, we consider day-to-night image translation using both traditional image processing approaches and deep learning models. This study employs a hybrid framework of traditional image processing followed by a CycleGANbased deep learning model for day-to-night image translation. We then conduct a comparative study on various generator architectures in our CycleGAN model. This research compares four different CycleGAN models; i.e., the orginal CycleGAN, feature pyramid network (FPN) based CycleGAN, the original U-Net vision transformer based UVCGAN, plus a modified UVCGAN with additional edge loss. The experimental results show that the orginal UVCGAN obtains an Frechet inception distance (FID) score of 16.68 and structural similarity index ´ measure (SSIM) of 0.42, leading in terms of FID. Meanwhile, FPN-CycleGAN obtains an FID score of 104.46 and SSIM score of 0.44, leading in terms of SSIM. Considering FPN-CycleGAN’s bad FID score and visual observation, we conclude that UVCGAN is more effective in generating synthetic nighttime images.

Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.

Food is a cornerstone of culture, shaping traditions and reflecting regional identities. However, understanding the nutritional content of diverse cuisines can be challenging due to the vast array of ingredients and the similarities in appearance across different dishes. While food provides essential nutrients for the body, excessive and unbalanced consumption can harm health. Overeating, particularly high-calorie and fatty foods, can lead to an accumulation of excess calories and fat, increasing the risk of obesity and related health issues such as diabetes and heart disease. This paper introduces a novel ensemble learning approach with a dictionary that contains food nutrition content for addressing this challenge, specifically on Padang cuisine, a rich culinary tradition from West Sumatera, Indonesia. By leveraging a dataset of nine Padang dishes, the system employs image enhancement techniques and combines deep feature extraction and machine learning algorithms to classify food items accurately. Then, depending on the classification results, the system evaluates the nutritional content and creates a dietary evaluation report that includes the amount of protein, fat, calories, and carbs. The model is evaluated using different evaluation metrics and achieving a state-of-the-art accuracy of 85.56%, significantly outperforming standard baseline models. Based on the findings, the suggested approach can efficiently classify different Padang dishes and produce dietary assessments, enabling personalised nutritional recommendations to provide clear information on a balanced diet to enhance physical and overall wellness.