Qing Lin

Chenxi Ma, Bo Yan, Qing Lin et al.

Deep neural networks have greatly promoted the performance of single image super-resolution (SISR). Conventional methods still resort to restoring the single high-resolution (HR) solution only based on the input of image modality. However, the image-level information is insufficient to predict adequate details and photo-realistic visual quality facing large upscaling factors (x8, x16). In this paper, we propose a new perspective that regards the SISR as a semantic image detail enhancement problem to generate semantically reasonable HR image that are faithful to the ground truth. To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities. Different from existing methods, the proposed TGSR could generate HR image details that match the text descriptions through a coarse-to-fine process. Extensive experiments and ablation studies demonstrate the effect of the TGSR, which exploits the text reference to recover realistic images.

Xuhao Jiang, Weimin Tan, Qing Lin et al.

In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much information, resulting in reconstructing high-quality image difficultly. To address this issue, we propose a multimodal fusion learning method for text-guided JPEG artifacts reduction, in which the corresponding text description not only provides the potential prior information of the highly compressed image, but also serves as supplementary information to assist in image deblocking. We fuse image features and text semantic features from the global and local perspectives respectively, and design a contrastive loss built upon contrastive learning to produce visually pleasing results. Extensive experiments, including a user study, prove that our method can obtain better deblocking results compared to the state-of-the-art methods.

Francois Drielsma, Qing Lin, Pierre Côte de Soux et al.

Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. Electromagnetic (EM) activity typically exhibits spatially detached fragments of varying morphology and orientation that are challenging to efficiently assemble using traditional algorithms. Similarly, particles that are spatially removed from each other in the detector may originate from a common interaction. Graph Neural Networks (GNNs) were developed in recent years to find correlations between objects embedded in an arbitrary space. The Graph Particle Aggregator (GrapPA) first leverages GNNs to predict the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e. primary fragments. On the PILArNet public LArTPC simulation dataset, the algorithm achieves achieves a shower clustering accuracy characterized by a mean adjusted Rand index (ARI) of 97.8 % and a primary identification accuracy of 99.8 %. It yields a relative shower energy resolution of $(4.1+1.4/\sqrt{E (\text{GeV})})\,\%$ and a shower direction resolution of $(2.1/\sqrt{E(\text{GeV})})^{\circ}$. The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99.2 % for an interaction density of $\sim\mathcal{O}(1)\,m^{-3}$.