Title: Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Venue: NeurIPS 2025
Accepted: yes
Keywords: infrared image

fusion network

benchmark
Abstract: We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradations, such as noise, contrast, and blurring, making it difficult to handle coupled degradations. Meanwhile, all-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness due to the significant differences in imaging models. In sight of this, we first revisit the imaging mechanism and introduce a Recurrent Prompt Fusion Network (RPFN). Specifically, the RPFN initially establishes prompt pairs based on the thermal imaging process. For each type of degradation, we fuse the corresponding prompt pairs to modulate the model's features, providing adaptive guidance that enables the model to better address specific degradations under single or multiple conditions.In addition, a selective recurrent training mechanism is introduced to gradually refine the model's handling of composite cases to align the enhancement process, which not only allows the model to remove camera noise and retain key structural details, but also enhancing the overall contrast of the thermal image. Furthermore, we introduce the most comprehensive high-quality infrared benchmark covering a wide range of scenarios. Extensive experiments substantiate that our approach not only delivers promising visual results under specific degradation but also significantly improves performance on complex degradation scenes, achieving a notable 8.76% improvement.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel Progressive Prompt Fusion Network (PPFN) alongside a Selective Progressive Training (SPT) strategy, aiming to enhance TIR images under complex composite degradation scenarios. Additionally, the authors construct a high-quality TIR dataset to facilitate model training and evaluation. Extensive experiments and ablation studies demonstrate that the proposed method outperforms SOTA approaches in both quantitative and qualitative metrics, while exhibiting superior robustness and generalization capability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The figures are well-designed and effectively illustrate the core contributions of the paper, enhancing the clarity and readability of the proposed methods.\n2. The proposed PPFN module and SPT strategy demonstrate strong capability in addressing complex degradation scenarios in infrared imagery, showcasing both innovation and practical effectiveness.\n3. The introduction of the HM-TIR dataset is a valuable contribution, offering high-quality TIR images that span a diverse range of environmental conditions and real-world scenarios, which can benefit future research in thermal imaging area.\n\nWeaknesses:\n1. While the proposed TIR imaging degradation simulation is novel within the context of thermal imaging, high-order degradation models have been extensively explored in the general super-resolution literatures [1-2]. The authors are encouraged to explicitly clarify how their proposed TIR-specific degradation differences in existing high-order degradation in the RGB domain.\n2. The related work section omits several recent advances in unified or all-in-one image restoration frameworks that address multiple degradation types within a single model. Incorporating a discussion of these works [3-5] would strengthen the contextual positioning of the proposed approach.\n3. The paper contains some unclear representations that hinder understanding. Specifically, the meaning of \"multiplication\" in Table 1 is ambiguous, and the small font size and pics in Figure 6 affects readability and the clarity of key results.\n4. The cost comparison analysis lacks critical implementation details, such as the hardware specifications (e.g., GPU model, memory capacity) and the resolution or size of the input images used during inference. These details are necessary to ensure a fair and reproducible comparison across different methods.\n\n[1] Wang, Xintao, et al. \"Real-esrgan: Training real-world blind super-resolution with pure synthetic data.\" ICCV Workshop. 2021.\n[2] Zhang, Kai, et al. \"Designing a practical degradation model for deep blind image super-resolution.\" ICCV. 2021.\n[3] Potlapalli, Vaishnav, et al. \"Promptir: Prompting for all-in-one image restoration.\" NeurIPS. 2023.\n[4] Jiang, Yitong, et al. \"Autodir: Automatic all-in-one image restoration with latent diffusion.\" ECCV. 2024.\n[5] Conde, Marcos V, et al. \"Instructir: High-quality image restoration following human instructions.\" ECCV. 2024.\n\nQUESTIONS:\nPlease refer to the weakness.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to address the challenges associated with various and hybrid degradations in infrared imaging. It proposes a novel approach to model the degradation sequence, which includes contrast, blurring, and noise. The key innovation is the introduction of the Progressive Prompt Fusion Network (PPFN), a module designed to effectively mitigate specific degradations, whether they occur individually or in combination. Additionally, the Selective Progressive Training (SPT) mechanism is introduced, enabling the model to better handle hybrid degradation conditions. Extensive experiments demonstrate that the proposed PPFN outperforms state-of-the-art methods, showcasing its superior effectiveness in tackling infrared image degradation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed plug-and-play PPFN module and the hybrid degradation training strategy are novel contributions to the field of infrared imaging.\n2. The newly introduced dataset is valuable and likely to benefit the broader infrared imaging research community.\n3. The paper is clearly written and well-organized, with informative and well-designed figures that effectively support the presentation of the proposed methods and results.\n\nWeaknesses:\n1. The authors claim that “this is the first study addressing TIR enhancement under such multifaceted degradation conditions” However, to the best of my knowledge, several existing works have explored infrared enhancement under hybrid degradation settings [1-3]. Please make a detailed discussion with these works from degradation pipeline setting. \n2. There is a lack of clarity in Table 1 regarding the dataset type used for each method. Specifically, whether the format is the single image or video frame. Please consider adding an additional row that explicitly indicates the dataset type for each method. \n3. The text size in Figure 6 and Table 3 is too small, making it difficult to read, especially when printed or viewed on standard screens. Please revise these visual elements to improve readability and accessibility.\n\n[1] Liu, Li, Luping Xu, and Houzhang Fang. \"Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints.\" IEEE Transactions on Geoscience and Remote Sensing 58.3 (2019): 1777-1789.\n[2] Zhang, Duo and Liu, Yinnian and Zhao, Yun, et al. \"Algorithm research on detail and contrast enhancement of high dynamic infrared images.\" Applied Sciences 13.23 (2023): 12649.\n[3] Cai, Lijing and Dong, Xiangyu and Zhou, Kailai, et al. \"Exploring video denoising in thermal infrared imaging: physics-inspired noise generator, dataset and model.\" IEEE Transactions on Image Processing (2024).\n\nQUESTIONS:\nPlease refer to the weaknesses. In addition, I would like to ask the following questions for further clarification. 1. The degradation sequence modeling follows a fixed pipeline. Does the dynamic degradation modeling be more adaptive?  2. The meaning of the \"multiplication\" in camera angles in Table 1 is unclear. Could the authors clarify whether this refers to a combinatorial setup of camera angles or some other formulation?\n\nLIMITATIONS:\nNo.  While the paper aims to address a wide range of degradation types in TIR domain, it appears that some extreme and practically relevant weather conditions, such as heavy rain, are not explicitly considered in the current degradation pipeline. It is recommended that the authors discuss this type of degradation in the paper, including whether it can be addressed by their current framework or if additional modeling would be required.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Progressive Prompt Fusion Network (PPFN) for enhancing infrared images with complex degradation types. Additionally, a high-quality, multi-scene benchmark dataset is constructed. Within PPFN, degradation-specific and type-specific dual prompts are introduced to identify degradation types and decouple composite degradations into individual ones. Subsequently, a plug-and-play prompt fusion block is incorporated to integrate prompts into the learning process. Finally, a Selective Progressive Training (SPT) mechanism is designed to iteratively handle different degradations. Extensive experiments demonstrate that the proposed method exhibits significant advantages in complex degradation scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1) A high-quality infrared benchmark dataset covering diverse scenes is established, providing substantial support for advancement in this field.\n\n(2) The proposed PPFN incorporates prompt learning and introduces an SPT mechanism, enabling iterative enhancement of infrared images with complex composite degradations. \n\n(3) Extensive comparative and ablation experiments validate the effectiveness of the proposed method.\n\n(4) The well-crafted figures and well-organized writing make the proposed approach easily understandable.\n\nWeaknesses:\n\n(1) In the Introduction, the authors state: “Firstly, enhancement techniques developed for visible images... imaging processes”. However, the proposed method appears not specifically designed for the characteristics of infrared images. \n\n(2) The distinctions and advantages of the proposed method compared to existing approaches are not clearly demonstrated.\n\n(3) The iterative degradation removal approach seems functionally similar to cascading multiple degradation-specific networks. Compared to “all-in-one” methods, this design appears cumbersome and complex.\n\n(4) This framework cannot address unknown degradations and is limited to enhancing infrared images with predefined degradation types.\n\nQUESTIONS:\n(1) Compared to “all-in-one” methods designed for visible images, what advantages does the proposed framework offer? Theoretically, this framework does not appear specifically tailored for infrared images.\n\n(2) How does the iterative degradation removal framework fundamentally differ from cascading multiple degradation-specific networks? What are its distinctive advantages?\n\n(3) During prompt processing, how are degradation-specific prompts and type-specific prompts acquired? Are they generated using other language models or manually annotated?\n\n(4) Experiments on other datasets only provide visual results and lack quantitative analysis, which is insufficient to demonstrate the generalization capability of the proposed method.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes the Progressive Prompt Fusion Network (PPFN) for enhancing thermal infrared (TIR) images, which utilizes type and degradation-specific prompts along with a Selective Progressive Training (SPT) mechanism to adaptively guide the model in handling both single and composite degradations. Extensive experiments demonstrate that PPFN significantly outperforms existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n(1)\tThe PPFN establishes prompt pairs based on the thermal imaging process to better address specific degradations under single or multiple conditions.\n\n(2)\tA Selective Progressive Training (SPT) mechanism is proposed to refine the model's ability to process complex degradations iteratively. \n\n(3)\tThe paper also establishes a high-quality, multi-scenario TIR benchmark (HM-TIR) to facilitate comprehensive evaluation. \n\nWeaknesses:\n\n(1)\tSPT assumes that the degradation process occurs in a specific order. If the actual degradation sequence does not match the assumed order, the model’s performance may be affected.\n\n(2)\tIf the infrared image simultaneously contains stripe noise, optical noise, and Gaussian noise, how does SPT handle these three types of degradation?\n\n(3)\tThe paper does not specify how the two prompts are obtained.\n\n(4)\tWhat are the imaging differences between TIR and RGB images? Why would methods designed for the RGB image domain introduce artifacts, and how does the proposed method address these differences?\n\nQUESTIONS:\n(1)\tSPT assumes that the degradation process occurs in a specific order. If the actual degradation sequence does not match the assumed order, the model’s performance may be affected.\n\n(2)\tIf the infrared image simultaneously contains stripe noise, optical noise, and Gaussian noise, how does SPT handle these three types of degradation?\n\n(3)\tThe paper does not specify how the two prompts are obtained.\n\n(4)\tWhat are the imaging differences between TIR and RGB images? Why would methods designed for the RGB image domain introduce artifacts, and how does the proposed method address these differences?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}