Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing

Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these approaches prove computationally expensive, raising concern regarding their applicability to on-the-edge use-cases. This work introduces a simple, lightweight, and efficient framework for single-image haze removal, exploiting rich "dark-knowledge" information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation. We designed a feature affinity module to maximize the flow of rich feature semantics from the super-resolution teacher to the student dehazing network. In order to evaluate the efficacy of our proposed framework, its performance as a plug-and-play setup to a baseline model is examined. Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains. The extensive qualitative and quantitative results provided establish the effectiveness of the framework, achieving gains of upto 15\% (PSNR) while reducing the model size by $\sim$20 times.Comment: Preprint version. Accepted at Opti

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works

Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

Prediction of Gate to Gate block time for scheduled flights is considered as one of the challenging tasks in Air Traffic Flow Management (ATFM)system. Establishing an effective and practically reliable model to manage the problem of block time variation is a significant work. The airlines do tend to pad or inflate block time to Actual Block time to calculate Schedule block times which is approved by aviation regulator. This will lead to flaws in air traffic flow strategic decision-making and in turn affect the efficiency, estimation and undesirable delays, which leads to traffic congestion and inefficient ground delay programs. This study evaluates the effectiveness of nonlinear and time varying regression models to predict block time with minimal attributes in order to solve the problem of difficulty in predicting the block time variation. The key research outcome of this paper is to trace the temporal variations of flying time for different aircraft types and to predict the variation of actual arrival time from the scheduled arrival time at the destination airport. Ultimately, a combination of M5P regression model and logistic regression model is proposed to predict early, delayed and on-time conformity with approved schedules. Analysis based on a realistic data set of a domestic airport pair (Mumbai International Airport and New Delhi International Airport) in India shows that the proposed model is able to predict in block time at the time of departure with an accuracy of minutes for of test instances. As a result of the scheduled arrival time performance (early, delayed and timely) has been classified accurately using Logistic regression Classifier of machine learning. The test results show that the proposed model uses a minimum number of attributes and less computational time to more accurately predict the actual arrival time and scheduled arrival performance without details on the weather

MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction