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Yukang Zhang, Lei Tan, Yang Lu, Yan Yan, Hanzi Wang

Pedestrian attribute recognition (PAR) has received increasing attention due to its wide application in video surveillance and pedestrian analysis. Some text-enhanced methods tackle this task by converting attributes into language descriptions to facilitate interactive learning between attributes and visual images. However, these generic languages fail to uniquely describe different pedestrian images, missing individual characteristics. In this paper, we propose a Joint Implicit and Explicit Language Guidance Enhancement Learning (JGEL) method, which converts each pedestrian image into a language description with dual language learning to effectively learn enhanced attribute information. Specifically, we first propose an Implicit Language Guidance Learning (ILGL) stream. It projects visual image features into the text embedding space to generate pseudo-word tokens, implicitly modeling image attributes and providing personalized descriptions. Moreover, we propose an Explicit Attribute Enhancement Learning (EAEL) stream to guide the generated pseudo-word tokens obtained by ILGL explicitly aligned with pedestrian attributes, which can effectively align the pseudo-word tokens with the attribute concepts in the text embedding space. Extensive experiments show that JGEL has significant advantages in improving the performance of PAR and the challenging zero-shot PAR task.

Yongshan Zhang, Zixuan Zhang, Xinxin Wang, Lefei Zhang, Zhihua Cai

Cross-scene hyperspectral image (HSI) recognition aims to assign a unique label to each pixel in the target scene by transferring knowledge from the source scene. Existing methods primarily rely on fully labeled source data and either partially labeled or unlabeled target data. No prior work has addressed the more challenging scenario of cross-scene recognition without label guidance in both scenes. To bridge this gap, we present the first study on cross-scene HSI clustering, proposing an anchor-guided discriminative subspace alignment and clustering (ADSAC) framework that follows a well-structured three-step learning paradigm to effectively mitigate distribution shifts. Specifically, we first develop an anchor-promoted graph learning (APGL) model to efficiently derive accurate clustering labels for the source scene by leveraging anchor-based structural information. Next, we propose a discriminative cross-scene subspace alignment (DCSA) model to improve feature discriminability and reduce distribution discrepancies. Finally, labels of the target scene are inferred after source clustering and cross-scene alignment. To solve the formulated models, we design tailored optimization algorithms to ensure high-quality learning. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art methods.

Yizhuo Zhang, Kun Sun, Chang Tang, Yuanyuan Liu, Xin Li

The task of image feature matching aims to establish correct correspondences between images from two different views. While approaches based on attention mechanisms have demonstrated remarkable advancements in image feature matching, they still encounter substantial limitations. Specifically, current graph attention network approaches face performance bottlenecks in complex scenarios, such as low-texture regions or occlusions. This limitation stems from the self-attention mechanism, which, when lacking effective guidance, can lead to divergent attention weights or incorrect focus on regions with low discriminability, resulting in matching failures in low-texture environments. Inspired by how humans focus on distinctive regions when performing cross-view matching, we enhance attention to singular points in images that are salient, unique and have high cross-view matching potential during information aggregation, thereby improving matching capability. To realize the aforementioned strategies, we develop a novel Singularity-enhanced Graph Attention Network (SGAT). SGAT leverages Co-potentiality and Multi-Scale Singularity as prior guidance, and designs a Singularity-aware Attention mechanism and a Co-potentiality Guided Attention mechanism , specifically enhancing the perception of singularity and matching potential during feature interaction. Experimental results on multiple datasets, including ScanNet1500, demonstrate that our method outperforms current state-of-the-art sparse matching methods. In particular, the improvement is most pronounced in complex scenarios such as low-texture environments, significantly enhancing the accuracy and robustness of image matching and its downstream tasks.

Yiwei Zhang, Jin Gao, Hanshi Wang, Fudong Ge, Guan Luo, Weiming Hu, Zhipeng Zhang

Label assignment is a critical component in object detectors, particularly within DETR-style frameworks where the one-to-one matching strategy, despite its end-to-end elegance, suffers from slow convergence due to sparse supervision. While recent works have explored one-to-many assignments to enrich supervisory signals, they often introduce complex, architecture-specific modifications and typically focus on a single auxiliary strategy, lacking a unified and scalable design. In this paper, we first systematically investigate the effects of ``one-to-many'' supervision and reveal a surprising insight that performance gains are driven not by the sheer quantity of supervision, but by the diversity of the assignment strategies employed. This finding suggests that a more elegant, parameter-efficient approach is attainable. Building on this insight, we propose LoRA-DETR, a flexible and lightweight framework that seamlessly integrates diverse assignment strategies into any DETR-style detector. Our method augments the primary network with multiple Low-Rank Adaptation (LoRA) branches during training, each instantiating a different one-to-many assignment rule. These branches act as auxiliary modules that inject rich, varied supervisory gradients into the main model and are discarded during inference, thus incurring no additional computational cost. This design promotes robust joint optimization while maintaining the architectural simplicity of the original detector. Extensive experiments on different baselines validate the effectiveness of our approach. Our work presents a new paradigm for enhancing detectors, demonstrating that diverse ``one-to-many'' supervision can be integrated to achieve state-of-the-art results without compromising model elegance.

Yipeng Zhang, Yifan Liu, Zonghao Guo, Yidan Zhang, Xuesong Yang, Xiaoying Zhang, Chi Chen, Jun Song, Yuan Yao, Tat-Seng Chua 等

Vision transformers (ViTs) are widely employed in multimodal large language models (MLLMs) for visual encoding. However, they exhibit inferior performance on tasks regarding fine-grained visual perception. We attribute this to the inner limitations of ViTs in capturing diverse visual semantic levels. To address this, we present Hierarchical window (Hiwin) transformer as a plug-and-play solution for MLLMs, centered around our inverse semantic pyramid (ISP). Hiwin transformer comprises two key modules: (i) a visual detail injection module, which progressively injects low-level visual details into high-level language-aligned semantics features, thereby constructing an ISP, and (ii) a hierarchical window attention module, which leverages cross-scale windows to condense multi-level semantics from the ISP. Notably, our design achieves an average boost of 3.7% across 14 benchmarks compared with the baseline method, 9.3% on DocVQA for instance.

Yingkai Zhang, Tao Zhang, Jing Nie, Ying Fu

Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs. Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks. However, the noise in captured HSIs is usually complex and difficult to model accurately, which significantly limits the effectiveness of these approaches. In this paper, we propose a multi-stage noise-decoupling framework that decomposes complex noise into explicitly modeled and implicitly modeled components. This decoupling reduces the complexity of noise and enhances the learnability of HSI denoising methods when applied to real paired data. Specifically, for explicitly modeled noise, we utilize an existing noise model to generate paired data for pre-training a denoising network, equipping it with prior knowledge to handle the explicitly modeled noise effectively. For implicitly modeled noise, we introduce a high-frequency wavelet guided network. Leveraging the prior knowledge from the pre-trained module, this network adaptively extracts high-frequency features to target and remove the implicitly modeled noise from real paired HSIs. Furthermore, to effectively eliminate all noise components and mitigate error accumulation across stages, a multi-stage learning strategy, comprising separate pre-training and joint fine-tuning, is employed to optimize the entire framework. Extensive experiments on public and our captured datasets demonstrate that our proposed framework outperforms state-of-the-art methods, effectively handling complex real-world noise and significantly enhancing HSI quality.

Yin Zhang, Yongqiang Zhang, Yaoyue Zheng, Bogdan Raducanu, Dan Liu

Fine-tuning Vision Foundation Models (VFMs) with a small number of parameters has shown remarkable performance in Domain Generalized Semantic Segmentation (DGSS). Most existing works either train lightweight adapters or refine intermediate features to achieve better generalization on unseen domains. However, they both overlook the fact that long-term pre-trained VFMs often exhibit artifacts, which hinder the utilization of valuable representations and ultimately degrade DGSS performance. Inspired by causal mechanisms, we observe that these artifacts are associated with non-causal factors, which usually reside in the low- and high-frequency components of the VFM spectrum. In this paper, we explicitly examine the causal and non-causal factors of features within VFMs for DGSS, and propose a simple yet effective method to identify and disentangle them, enabling more robust domain generalization. Specifically, we propose Causal-Tune, a novel fine-tuning strategy designed to extract causal factors and suppress non-causal ones from the features of VFMs. First, we extract the frequency spectrum of features from each layer using the Discrete Cosine Transform (DCT). A Gaussian band-pass filter is then applied to separate the spectrum into causal and non-causal components. To further refine the causal components, we introduce a set of causal-aware learnable tokens that operate in the frequency domain, while the non-causal components are discarded. Finally, refined features are transformed back into the spatial domain via inverse DCT and passed to the next layer. Extensive experiments conducted on various cross-domain tasks demonstrate the effectiveness of Causal-Tune. In particular, our method achieves superior performance under adverse weather conditions, improving +4.8% mIoU over the baseline in snow conditions.

Yiming Zhang, Rui Yan, Xiaohua Wan, Yifan Zhao, Shuang Feng, Zhetao Xu, Ying Wang, Fa Zhang, Bin Hu

Cytological images originate from exfoliated cells, collected via liquid-based slides and digitized into whole slide images (WSIs). Unlike histological WSIs that exhibit continuous and well-structured tissue, cytological WSIs are sparse in spatial distribution and unstructured in cellular relationships. Typically, the nucleus serves as the primary diagnostic feature, while surrounding cytoplasmic information plays a supportive role. These unique characteristics limit the development of effective foundation models and hinder the transferability of histology-based models for cytopathology. To address this, we propose **Cyto-SSL**, the first self-supervised pretraining framework for cytological images. It introduces **Nuclei-Centered Perturbation**, which highlights individual nuclei by perturbing non-nuclear regions. We also design an SR-Transformer module, which complements this by using sparse attention to concentrate on diagnostically relevant scattered cells, while iRPE helps model to capture local spatial relationships and avoids unnecessary attention to irrelevant global structures. Experimental results show that **Cyto-SSL** enhances performance across diverse cytological datasets and Multiple Instance Learning (MIL) methods. On a WSI-level dataset, it achieved 95.67% accuracy and outperformed ImageNet-pretrained ResNet-50 by 11.33%, demonstrating superior feature representation for cytological analysis. Additionally, **Cyto-SSL** modules are plug-and-play, easily integrated into other pretraining frameworks, yielding a 2.6% accuracy gain across different SSL methods.

Yichi Zhang, Wenbo Zhang, Zehui Ling, Gang Feng, Sisi Peng, Deshu Chen, Yuchen Liu, Hongwei Zhang, Shuqi Wang, Lanlan Li 等

Positron emission tomography (PET) is a cornerstone of modern oncologic and neurologic imaging, distinguished by its unique ability to illuminate dynamic metabolic processes that transcend the anatomical focus of traditional imaging technologies. Radiology reports are essential for clinical decision making, yet their manual creation is labor-intensive and time-consuming. Recent advancements of vision-language models (VLMs) have shown strong potential in medical applications, presenting a promising avenue for automating report generation. However, existing applications of VLMs in the medical domain have predominantly focused on structural imaging modalities, while the unique characteristics of molecular PET imaging have largely been overlooked. To bridge the gap, we introduce PET2Rep, a large-scale comprehensive benchmark for evaluation of general and medical VLMs for radiology report generation for PET images. PET2Rep stands out as the first dedicated dataset for PET report generation with metabolic information, uniquely capturing whole-body image-report pairs that cover dozens of organs to fill the critical gap in existing benchmarks and mirror real-world clinical comprehensiveness. In addition to widely recognized natural language generation metrics, we introduce a series of clinical efficiency metrics to evaluate the quality of radiotracer uptake pattern description in key organs in generated reports. We conduct a head-to-head comparison of 30 cutting-edge general-purpose and medical-specialized VLMs. The results show that the current state-of-the-art VLMs perform poorly on PET report generation task, falling considerably short of fulfilling practical needs. Moreover, we identify several key insufficiency that need to be addressed to advance the development in medical applications. We believe PET2Rep will serve as a platform for the development and application of VLMs for PET imaging, accelerating the development of trustworthy reporting tools that can genuinely alleviate radiologist burden and enhance patient care.

Yafei Zhang, Shuaitian Song, Huafeng Li, Shujuan Wang, Yu Liu

In real-world vision systems, haze removal is required not only to enhance image visibility but also to meet the specific needs of diverse downstream tasks. To address this challenge, we propose a novel adaptive dynamic dehazing framework that incorporates a closed-loop optimization mechanism. It enables feedback-driven refinement based on downstream task performance and user instruction–guided adjustment during inference, allowing the model to satisfy the specific requirements of multiple downstream tasks without retraining. Technically, our framework integrates two complementary and innovative mechanisms: (1) a task feedback loop that dynamically modulates dehazing outputs based on performance across multiple downstream tasks, and (2) a text instruction interface that allows users to specify high-level task preferences. This dual-guidance strategy enables the model to adapt its dehazing behavior after training, tailoring outputs in real time to the evolving needs of multiple tasks. Extensive experiments across various vision tasks demonstrate the strong effectiveness, robustness, and generalizability of our approach. These results establish a new paradigm for interactive, task-adaptive dehazing that actively collaborates with downstream applications.

Yachong Zhang, Lei Meng, Shuo Xu, Zhuang Qi, Wei Wu, Lei Wu, Xiangxu Meng

Video classification requires event-level representations of objects and their interactions. Existing methods typically rely on data-driven approaches, which either learn such features from whole frames or object-centric visual regions. Therefore, the modeling of spatiotemporal interactions among objects is usually overlooked. To address this issue, this paper presents a Decomposition of Synergistic, Unique, and Redundant Causal Representations Learning (SurdCRL) model for video classification, which introduces a newly-proposed SURD causal theory to model the spatiotemporal features of both object dynamics and their in- and cross-frame interactions. Specifically, SurdCRL employs three modules to model the object-centric spatiotemporal dynamics using distinct types of causal components, where the first module Spatial-Temporal Entity Modeling decouples the frame into object and context entities, and employs a temporal message passing block to capture object state changes over time, generating spatiotemporal features as basic causal variables. Second, the Dual-Path Causal Inference module mitigates confounders among causal variables by front-door and back-door interventions, thus enabling the subsequent causal components to reflect their intrinsic effects. Finally, the Causal Composition and Selection module employs the compositional structure-aware attention to project the causal variables and their high-order interactions into the synergistic, unique, and redundant components. Experiments on two benchmarking datasets verify that SurdCRL better captures event-relevant object-centric representation by decomposing spatiotemporal object interactions into three types of causal components.

Xuanyu Zhang, Weiqi Li, Shijie Zhao, Junlin Li, Li Zhang, Jian Zhang

Recent advances in AI-generated content (AIGC) have led to the emergence of powerful text-to-video generation models. Despite these successes, evaluating the quality of AIGC-generated videos remains challenging due to limited generalization, lack of temporal awareness, heavy reliance on large-scale annotated datasets, and the lack of effective interaction with generation models. Most current approaches rely on supervised fine-tuning of vision-language models (VLMs), which often require large-scale annotated datasets and tend to decouple understanding and generation. To address these shortcomings, we propose VQ-Insight, a novel reasoning-style VLM framework for AIGC video quality assessment. Our approach features: (1) a progressive video quality learning scheme that combines image quality warm-up, general task-specific temporal learning, and joint optimization with the video generation model; (2) the design of multi-dimension scoring rewards, preference comparison rewards, and temporal modeling rewards to enhance both generalization and specialization in video quality evaluation. Extensive experiments demonstrate that VQ-Insight consistently outperforms state-of-the-art baselines in preference comparison, multi-dimension scoring, and natural video scoring, bringing significant improvements for video generation tasks.

Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang

Recently, All-in-One image restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches heavily rely on degradation-specific representation learning, which can lead to oversmoothing and artifacts in the restored images. To address this limitation, we propose ClearAIR, a novel AiOIR framework inspired by human visual perception and designed with a hierarchical restoration strategy in a coarse-to-fine manner. First, leveraging the global priority characteristic of early human visual perception, we employ an image quality assessment model to evaluate the overall image structure and degradation level. Next, we introduce a Semantic Guidance Unit to provide coarse semantic region guidance and a Task Identifier to predict local degradation types, enabling a more informed characterization of local degradation patterns. Finally, aiming at the challenge of local detail restoration, we propose an Internal Clue Reuse Mechanism that deeply mines the internal information of the image in a self-supervised manner to enhance the model’s capacity for fine-detail recovery. Experimental results demonstrate that ClearAIR achieves superior restoration performance across diverse synthetic and real-world datasets.

Xu Zhang, Jianzhong Huang, Lefei Zhang

Recent advances in controllable text-to-image (T2I) generation have achieved impressive results in natural images, but remote sensing (RS) T2I remains challenging due to the unique nature of geospatial data. Existing methods struggle to integrate diverse spatial controls and model complex spatial relationships, often failing to maintain semantic consistency with typically vague or incomplete textual descriptions. Moreover, limited by small-scale, low-quality datasets, these models produce outputs with inconsistent layouts and unrealistic content. To address these issues, we propose Any2RSI, a flexible framework for controllable RS T2I generation. It features a Cross-Modal Multi-Control Adapter that extracts modality-agnostic embeddings from heterogeneous spatial inputs, enabling precise spatial guidance. To compensate for sparse or ambiguous text prompts, we introduce a VLM-Empowered Enriched Description Generation module that enhances input descriptions with cross-modal semantics for more coherent image generation. Furthermore, we present RST2I-110K, a new large-scale dataset with over 115,000 high-quality RS image-text pairs across diverse scenes, alleviating data scarcity in this domain. Extensive experiments show that Any2RSI achieves state-of-the-art performance on both existing and new datasets, improving the realism and structural accuracy of generated RS imagery.

Xiaohan Zhang, Zhangkai Shen, Si-Yuan Cao, Xiaokai Bai, Yiming Li, Zheheng Han, Zhe Wu, Qi Ming, Hui-Liang Shen

We present MoP-UAV, a new benchmark for UAV-based cross-view object geo-localization guided by multi-modal prompts. MoP-UAV supports fine-grained object-level cross-view localization under diverse prompt modalities, including natural language, bounding boxes, and click points. It offers potential for incorporating large foundation models like large language models (LLMs) and promotes the building of more flexible and intelligent UAV agents. Based on the benchmark, we propose MoPT, a multi-modal-prompt-guided tansformer that embeds prompts as token sequences and extract object location from UAV and satellite features via cross-attention. To enhance semantic consistency and performance, we further adopt a cross-view contrastive loss and propose a RefCOCOg-based pre-training strategy. Extensive experiments show that MoPT achieves robust localization under arbitrary prompt combinations. Notably, multi-modal-prompt training significantly boosts unimodal-prompt inference performance, highlighting the generalization benefits of multi-modal learning. MoPT trained with multi-modal prompts outperforms prior unimodal prompt works under the same setting.

Xiangyue Zhang, Jianfang Li, Jianqiang Ren, Jiaxu Zhang

Reliable co-speech motion generation requires precise motion representation and consistent structural priors across all joints. Existing generative methods typically operate on local joint rotations, which are defined hierarchically based on the skeleton structure. This leads to cumulative errors during generation, manifesting as unstable and implausible motions at end-effectors. In this work, we propose GlobalDiff, a diffusion-based framework that operates directly in the space of global joint rotations for the first time, fundamentally decoupling each joint’s prediction from upstream dependencies and alleviating hierarchical error accumulation. To compensate for the absence of structural priors in global rotation space, we introduce a multi-level constraint scheme. Specifically, a joint structure constraint introduces virtual anchor points around each joint to better capture fine-grained orientation. A skeleton structure constraint enforces angular consistency across bones to maintain structural integrity. A temporal structure constraint utilizes a multi-scale variational encoder to align the generated motion with ground-truth temporal patterns. These constraints jointly regularize the global diffusion process and reinforce structural awareness. Extensive evaluations on standard co-speech benchmarks show that GlobalDiff generates smooth and accurate motions, improving the performance by 46.0% compared to the current SOTA under multiple speaker identities.

Wenrui Zhang, Xinggang Wang, Bin Feng, Wenyu Liu

Optical Chemical Structure Recognition (OCSR) plays a pivotal role in modern chemical informatics, enabling the automated conversion of chemical structure images from scientific literature, patents, and educational materials into machine-readable molecular representations. This capability is essential for large-scale chemical data mining, drug discovery pipelines, and Large Language Model (LLM) applications in related domains. However, existing OCSR systems face significant challenges in accurately recognizing stereochemical information due to the subtle visual cues that distinguish stereoisomers, such as wedge and dash bonds, ring conformations, and spatial arrangements. To address these challenges, we propose MolSight, a comprehensive learning framework for OCSR that employs a three-stage training paradigm. In the first stage, we conduct pre-training on large-scale but noisy datasets to endow the model with fundamental perception capabilities for chemical structure images. In the second stage, we perform multi-granularity fine-tuning using datasets with richer supervisory signals, systematically exploring how auxiliary tasks—specifically chemical bond classification and atom localization—contribute to molecular formula recognition. Finally, we employ reinforcement learning for post-training optimization and introduce a novel stereochemical structure dataset. Remarkably, we find that even with MolSight's relatively compact parameter size, the Group Relative Policy Optimization (GRPO) algorithm can further enhance the model's performance on stereomolecular. Through extensive experiments across diverse datasets, our results demonstrate that MolSight achieves state-of-the-art performance in (stereo)chemical optical structure recognition.

Wei Zhang, Yeying Jin, Xin Li, Yan Zhang, Xiaofeng Cong, Cong Wang, Fengcai Qiao, Zhichao Lian

Image-based virtual try-on (VTON) aims to synthesize photorealistic images of a person wearing specified garments. Despite significant progress, building a universal VTON framework that can flexibly handle diverse and complex tasks remains a major challenge. Recent methods explore multi-task VTON frameworks guided by textual instructions, yet they still face two key limitations: (1) semantic gap between text instructions and reference images, and (2) data scarcity in complex scenarios. To address these challenges, we propose UniFit, a universal VTON framework driven by a Multimodal Large Language Model (MLLM). Specifically, we introduce an MLLM-Guided Semantic Alignment Module (MGSA), which integrates multimodal inputs using an MLLM and a set of learnable queries. By imposing a semantic alignment loss, MGSA captures cross-modal semantic relationships and provides coherent and explicit semantic guidance for the generative process, thereby reducing the semantic gap. Moreover, by devising a two-stage progressive training strategy with a self-synthesis pipeline, UniFit is able to learn complex tasks from limited data. Extensive experiments show that UniFit not only supports a wide range of VTON tasks, including multi-garment and model-to-model try-on, but also achieves state-of-the-art performance.

Wei Zhang, Jiajun Chu, Xinci Liu, Chen Tong, Xinyue Li

Spatial Transcriptomics (ST) is a technology that measures gene expression profiles within tissue sections while retaining spatial context. It reveals localized gene expression patterns and tissue heterogeneity, both of which are essential for understanding disease etiology. However, its high cost has driven efforts to predict spatial gene expression from whole slide images. Despite recent advancements, current methods still face significant limitations, such as under-exploitation of high-level biological context, over-reliance on exemplar retrievals, and inadequate alignment of heterogeneous modalities. To address these challenges, we propose DKAN, a novel Dual-path Knowledge-Augmented contrastive alignment Network that predicts spatially resolved gene expression by integrating histopathological images and gene expression profiles through a biologically informed approach. Specifically, we introduce an effective gene semantic representation module that leverages the external gene database to provide additional biological insights, thereby enhancing gene expression prediction. Further, we adopt a unified, one-stage contrastive learning paradigm, seamlessly combining contrastive learning and supervised learning to eliminate reliance on exemplars, complemented with an adaptive weighting mechanism. Additionally, we propose a dual-path contrastive alignment module that employs gene semantic features as dynamic cross-modal coordinators to enable effective heterogeneous feature integration. Through extensive experiments across three public ST datasets, DKAN demonstrates superior performance over state-of-the-art models, establishing a new benchmark for spatial gene expression prediction and offering a powerful tool for advancing biological and clinical research.

Tongshun Zhang, Pingping Liu, Zijian Zhang, Qiuzhan Zhou

Current dark image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: computational burden and error correction costs associated with reliance on external priors (manual or cross-modal); redundant operations in complex multi-stage enhancement pipelines; and indiscriminate processing across frequency components in frequency-domain methods, leading to excessive global computational demands. To address these challenges, we propose an Efficient Self-Mining Prior-Guided Joint Frequency Enhancement Network (SPJFNet). Specifically, we first introduce a Self-Mining Guidance Module (SMGM) that generates lightweight endogenous guidance directly from the network, eliminating dependence on external priors and thereby bypassing error correction overhead while improving inference speed. Second, through meticulous analysis of different frequency domain characteristics, we reconstruct and compress multi-level operation chains into a single efficient operation via lossless wavelet decomposition and joint Fourier-based advantageous frequency enhancement, significantly reducing parameters. Building upon this foundation, we propose a Dual-Frequency Guidance Framework (DFGF) that strategically deploys specialized high/low frequency branches (wavelet-domain high-frequency enhancement and Fourier-domain low-frequency restoration), decoupling frequency processing to substantially reduce computational complexity. Rigorous evaluation across multiple benchmarks demonstrates that SPJFNet not only surpasses state-of-the-art performance but also achieves significant efficiency improvements, substantially reducing model complexity and computational overhead.