Ashraful Alam

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an advanced colour vision technique and introduces an efficient algorithm called ColorPix2Pix for normalizing images captured in various hazardous environmental and lighting conditions. Optimized Generative Adversarial Network (GAN) models were employed to address these challenges by normalizing images affected by extreme lighting and weather conditions. Specifically, a novel model, the ColorPix2Pix GAN, was proposed, incorporating an enhanced loss function designed to prioritize both structural and color fidelity. Through a custom loss calculation, the GAN is guided to reconstruct images more effectively, reducing noise and restoring essential visual details while maintaining natural color profiles. The methodology included a two-phase training process in which two comprehensive datasets were curated, capturing diverse environmental scenarios, such as fog, rain, and variable illumination conditions, to simulate real-world challenges for AVs. These datasets enabled robust training and testing of the ColorPix2Pix model to assess consistent performance across complex scenarios. Furthermore, a custom loss function combining perceptual loss with color consistency measures was used to increase the GAN’s sensitivity to both structural accuracy and color fidelity, allowing the model to simulate the natural appearance of original images while effectively removing environmental noise. The model was trained under varying adverse conditions to enhance adaptability. For the lighting dataset, it achieved an average SSIM of 0.767, PSNR of 68.581, LOE of 0.171, and LPIPS of 0.205. On the weather dataset, it recorded an SSIM of 0.660, PSNR of 67.185, LOE of 0.177, and LPIPS of 0.241. These results highlight the model’s effectiveness in restoring image quality and improving perceptual reliability for autonomous vehicle systems, outperforming existing image normalization methods.

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.