10 accepted papers
All-in-one models for adverse weather removal aim to process various degraded images using a single set of parameters, making them ideal for real-world scenarios. However, they encounter two main challenges: catastrophic forgetting and limited degradation awareness. The former causes the model to lo…
Image restoration poses a garners substantial interest due to the exponential surge in demands for recovering high-quality images from diverse mobile camera devices, adverse lighting conditions, suboptimal shooting environments, and frequent image compression for efficient transmission purposes. Yet…
The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise. To tackle with this issue we present a novel strategy from the view of shadow transformation in this paper: directly homoge…
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly ad…
Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using the single set of network parameters. In this paper, we find that distorted images under different weather conditions contain general characteristics as well as their specific characteristic…
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxilia…
Deep Convolutional Neural Networks (CNNs) based methods have achieved significant breakthroughs in the task of single image shadow removal. However, the performance of these methods remains limited for several reasons. First, the existing shadow illumination model ignores the spatially variant prope…
Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial b…
"To meet the needs of practical applications, current deep learning-based methods focus on using a single model to handle JPEG images with different compression qualities, while few of them consider the auxiliary effects of the compression quality information. Recently, several methods estimate qual…
Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is…