Nitin Singh

Chandresh Sutariya, Nitin Singh

The simultaneous restoration of high-frequency details and suppression of severe noise in low-light imagery presents a significant and persistent challenge in computer vision. While large-scale Transformer models like SwinIR have set the state of the art in performance, their high computational cost can be a barrier for practical applications. This paper investigates the critical trade-off between performance and efficiency by comparing the state-of-the-art SwinIR model against a standard, lightweight Convolutional Neural Network (CNN) on this challenging task. Our experimental results reveal a nuanced but important finding. While the Transformer-based SwinIR model achieves a higher peak performance, with a Peak Signal-to-Noise Ratio (PSNR) of 39.03 dB, the lightweight CNN delivers a surprisingly competitive PSNR of 37.4 dB. Crucially, the CNN reached this performance after converging in only 10 epochs of training, whereas the more complex SwinIR model required 132 epochs. This efficiency is further underscored by the model's size; the CNN is over 55 times smaller than SwinIR. This work demonstrates that a standard CNN can provide a near state-of-the-art result with significantly lower computational overhead, presenting a compelling case for its use in real-world scenarios where resource constraints are a primary concern.

Nemanja Djuric, Vladan Radosavljevic, Henggang Cui et al.

We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings. We introduce a deep learning-based approach that takes into account a current world state and produces raster images of each actor's vicinity. The rasters are then used as inputs to deep convolutional models to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following completion of the offline tests the system was successfully tested onboard self-driving vehicles.