A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A^2Net) with lightweight architectures to remove raindrops from single images. Instead of directly cascading convolutional layers, we design an adjacent aggregation architecture to better fuse features for rich representations generation, which can lead to high quality images reconstruction. To further simplify the learning process, we utilize a problem-specific knowledge to force the network focus on the luminance channel in the YUV color space instead of all RGB channels. By combining adjacent aggregating operation with color space transformation, the proposed A^2Net can achieve state-of-the-art performances on raindrop removal with significant parameters reduction

Influence of Rain on Vision-Based Algorithms in the Automotive Domain

The Automotive domain is a highly regulated domain with stringent requirements that characterize automotive systems’ performance and safety. Automotive applications are required to operate under all driving conditions and meet high levels of safety standards. Vision-based systems in the automotive domain are accordingly required to operate at all weather conditions, favorable or adverse. Rain is one of the most common types of adverse weather conditions that reduce quality images used in vision-based algorithms. Rain can be observed in an image in two forms, falling rain streaks or adherent raindrops. Both forms corrupt the input images and degrade the performance of vision-based algorithms. This dissertation describes the work we did to study the effect of rain on the quality images and the target vision systems that use them as the main input. To study falling rain, we developed a framework for simulating failing rain streaks. We also developed a de-raining algorithm that detects and removes rain streaks from the images. We studied the relation between image degradation due to adherent raindrops and the performance of the target vision algorithm and provided quantitive metrics to describe such a relation. We developed an adherent raindrop simulator that generates synthetic rained images, by adding generated raindrops to rain-free images. We used this simulator to generate rained image datasets, which we used to train some vision algorithms and evaluate the feasibility of using transfer-learning to improve DNN-based vision algorithms to improve performance under rainy conditions.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/170924/1/Yazan Hamzeh final dissertation.pdfDescription of Yazan Hamzeh final dissertation.pdf : Dissertatio

GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions

Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration under adverse weather conditions. GridFormer is designed in a grid structure using a residual dense transformer block, and it introduces two core designs. First, it uses an enhanced attention mechanism in the transformer layer. The mechanism includes stages of the sampler and compact self-attention to improve efficiency, and a local enhancement stage to strengthen local information. Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer. This design further improves the network's ability to learn effective features from both preceding and current local features. The GridFormer framework achieves state-of-the-art results on five diverse image restoration tasks in adverse weather conditions, including image deraining, dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The source code and pre-trained models will be released.Comment: 17 pages, 12 figure