Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization

Recently, deep learning-based methods have dominated image dehazing domain. Although very competitive dehazing performance has been achieved with sophisticated models, effective solutions for extracting useful features are still under-explored. In addition, non-local network, which has made a breakthrough in many vision tasks, has not been appropriately applied to image dehazing. Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper. We start with extracting richer features for dehazing. Specifically, we design a multi-stream feature extraction (MSFE) sub-block, which contains three parallel convolutions with different receptive fields (i.e., $1\times 1$, $3\times 3$, $5\times 5$) for extracting multi-scale features. Following MSFE, we employ an attention sub-block to make the model adaptively focus on important channels/regions. The MSFE and attention sub-blocks constitute our MSFAB. Then, we design a cross non-local block (CNLB), which can capture long-range dependencies beyond the query. Instead of the same input source of query branch, the key and value branches are enhanced by fusing more preceding features. CNLB is computation-friendly by leveraging a spatial pyramid down-sampling (SPDS) strategy to reduce the computation and memory consumption without sacrificing the performance. Last but not least, a novel detail-focused contrastive regularization (DFCR) is presented by emphasizing the low-level details and ignoring the high-level semantic information in the representation space. Comprehensive experimental results demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters.Comment: submitted to IEEE TCYB for possible publicatio

Prompt-based test-time real image dehazing: a novel pipeline

Existing methods attempt to improve models' generalization ability on real-world hazy images by exploring well-designed training schemes (e.g., CycleGAN, prior loss). However, most of them need very complicated training procedures to achieve satisfactory results. In this work, we present a totally novel testing pipeline called Prompt-based Test-Time Dehazing (PTTD) to help generate visually pleasing results of real-captured hazy images during the inference phase. We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i.e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing. Accordingly, we first apply a prompt generation module (PGM) to generate a visual prompt, which is the source of appropriate statistical perturbations for mean and standard deviation. And then, we employ the feature adaptation module (FAM) into the existing dehazing models for adjusting the original statistics with the guidance of the generated prompt. Note that, PTTD is model-agnostic and can be equipped with various state-of-the-art dehazing models trained on synthetic hazy-clean pairs. Extensive experimental results demonstrate that our PTTD is flexible meanwhile achieves superior performance against state-of-the-art dehazing methods in real-world scenarios. The source code of our PTTD will be made available at https://github.com/cecret3350/PTTD-Dehazing.Comment: update github link (https://github.com/cecret3350/PTTD-Dehazing

Formulae of Beurling-Deny and Lejan For Non-Symmetric Dirichlet Forms

By the classical Beurling-Deny formula, any regular symmetric Dirichlet form is decomposed into the diffusion, jumping and killing parts. Further, the diffusion part is characterized by LeJan’s formula. In this paper, both the Beurling-Deny formula and LeJan’s formula are extended to regular non-symmetric Dirichlet forms. In addition, a counterexample is presented to show the gap in the Beurling-Deny formula for non-symmetric Dirichlet forms in the existing literatures

Extensions of Lévy-Khintchine Formula and Beurling-Deny Formula in Semi-Dirichlet Forms Setting

The Lévy-Khintchine formula or, more generally, Courrège’s theorem characterizes the infinitesimal generator of a Lévy process or a Feller process on Rd. For more general Markov processes, the formula that comes closest to such a characterization is the Beurling-Deny formula for symmetric Dirichlet forms. In this paper, we extend these celebrated structure results to include a general right process on a metrizable Lusin space, which is supposed to be associated with a semi-Dirichlet form. We start with decomposing a regular semi-Dirichlet form into the diffusion, jumping and killing parts. Then, we develop a local compactification and an integral representation for quasi-regular semi-Dirichlet forms. Finally, we extend the formulae of Lévy-Khintchine and Beurling-Deny in semi-Dirichlet forms setting through introducing a quasi-compatible metric

Broadcasting Quantum Fisher Information

It is well known that classical information can be cloned, but non-orthogonal quantum states cannot be cloned, and non-commuting quantum states cannot be broadcast. We conceive a scenario in which the object we want to broadcast is the statistical distinguishability, as quantified by quantum Fisher information, about a signal parameter encoded in quantum states. We show that quantum Fisher information cannot be cloned, whilst it might be broadcast even when the input states are non-commuting. This situation interpolates between cloning of classical information and no-broadcasting of quantum information, and indicates a hybrid way of information broadcasting which is of particular significance from both practical and theoretical perspectives.Comment: 5 pages. Improved version. Any comments is welcom

DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.Comment: Accepted by ACM International Conference on Web Search and Data Mining (WSDM'23