Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Authors: Jinyuan Liu
Zihang Chen
Zhu Liu
Zhiying Jiang
Long Ma
Xin Fan
Risheng Liu
Venue: NeurIPS 2025 poster ()
Keywords: infrared image
fusion network
benchmark
Primary area: applications
TL;DR: Infrared enhancement in real scenarios：BenchmarK and Solution

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.

Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Jinyuan Liu', Zihang Chen', Zhu Liu', Zhiying Jiang', Long Ma', Xin Fan', Risheng Liu
School of Software Engineering, Dalian University of Technology Information Science and Technology College, Dalian Martime University atlantis9 1at.stta000mmmmll11. com, chenzi hanggmaaanaaamal dlut. edu. en
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 effective- ness due to the significant differences in imaging models. In sight of this, we first revisit the imaging mechanism and introduce Progressive Prompt Fusion Network (PPFN). Specifically, the PPEN initially establishes prompt pairs based the thermal imaging process. For each type of degradation, we fuse the cor- responding prompt pairs to modulate the model's features, providing adaptive guidance that enables the model better address specific degradations under sin- gle or multiple conditions. In addition, a Selective Progressive Training (SPT) 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 re- move camera noise and retain key structural details, but also enhancing the overall contrast of the thermal image. Furthermore, multi-scenarios infrared benchmark covering a wide range 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 notable 8.76% improvement. Code is available at hhttp:://// :// thub. com/ www.oooo/iaaeeeee Zihang- Chen Hot//MMTTRR
introduce the most high-quality,
Introduction
Thermal Infrared (TIR) imaging captures images by detecting the thermal radiation emitted by objects, typically within the wavelength range of 14 mi- crometers. Unlike visible light imaging, TIR does not depend on external light sources, allowing it to function effectively in complete darkness or low-light conditions. Its ability to penetrate smoke, haze, and minor obstructions, coupled with accurate temper- ature data, makes TIR essential cations [69] 677, such object detection 32 35), semantic segmentation [68], and autonomous driv- ing [I).
Radiution
Atmosphhettc Sraihering
Turbullaace
diverse appli-
Figure 1: An illustration of the thermal infrared degradation pipeline. Thermal infrared imaging prone degradation from external factors such as solar radiation, atmospheric seattering, tur- bulence, as well as internal factors like pixel size, internal noise, and jitter.
Despite its advantages, TIR imaging faces significant challenges that limit its widespread use. The com-
CCorespondingg Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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plexity of the imaging process and the reliance on expensive, specialized materials like Mercury Cadmium Telluride (MCT) and Indium Antimonide (InSb) make obtaining hugl-quality TIR images difficult. Additionally, TIR systems are highly susceptible to external factors such as temperature fluctuations and varying atmospheric conditions, which can degrade image quality. These obstacles underscore the critical need to advance thermal infrared image enhancement techniques.
considerable number of image enhancement methods have been proposed for TIR or visible images. Techniques such as histogram equalization 48. adaptive filtering 49 [8 and deep learning-basee
approaches [23 12 36 have been utilized to improve image contrast, reduce noise, and enhance overall visual quality. However, these methods exhibit two major limitations. Firstly, enhancement techniques developed for visible images often prove challenging to apply to TIR images due to fundamental differences in imaging modalities, degradation and imaging processes. Secondly, existing enhancement methods only address single degradation, such as denoising or encontrast.
Moreover, a major obstacle TIR image enhancement is the limited availability of diverse datasets. Although learning-based ways have demonstrated success in various image processing applications, they require large and varied datasets to train effectively and generalize well. However, existing datasets encompass only a narrow range of scenes and conditions, making challenging validation.
Incorporating these criteria, this paper presents the Progressive Prompt Fusion Network (PPFN) for enhancing TIR images. PPFN comprises two key components: type and degradation-spest and a prompt fusion module. The degradation-speciff prompts guide the model in identifying degradation types, while type-specific prompts differentiate single from composite degradation scenarios. The prompt fusion module integrates prompt pairs to iteratively modulate model features, providing adaptive guidance tailored to specific degradation types in both single and multiple contexts. Additionally, we introduce a Selective Progressive Training (SPT) mechanism for handling composite and single degradations, which iteratively refines each degradation step by using the output from one stage as input for the next in composite scenarios, while applying standard training for single degradations. Consequently, the model effectively eliminates each impairment without interference, resulting in significant performance improvements. Our contributions can be summarized into four key aspects, as follows:
prompts
* We propose PPFN to enhance TIR images, delivering exceptional visual quality in hybrid degradations. To our knowledge, this is the first study addressing TIR enhancement under such multifaceted degradation conditions.
Addressing intricate degradations in real-world thermal infrared images, we introduce prompt fusion block that incorporates prior knowledge into the learning process, effectively managing both single and hybrid degradations. Importantly, the prompt fusion block is plug-and-play module that seamlessly integrates into various existing network architectures, enhancing performance.
We propose SPT scheme that optimizes both single and hybrid degradation scenarios, enabling the model to effectively refine complex degradations while ensuring robustness and stability under simpler conditions.
We establish high-quality TIR benchmark covering multiple scenarios, named HM-TIR, with all collected images meticulously focused for clarity. This dataset encompasses diverse environments, including urban areas, forests, and oceans, to name a few.
2
Related Work
This section provides a concise overview of existing TIR and visible image enhancement techniques relevant to our study, as well as the necessary benchmarks for learning and empirical evaluation.
2.1 TIR/Visible Image Enhancement
With the growing demands of modern applications, numerous TIR image enhancement methods have been developed, achieving promising results. For TIR denoising, studies 33
have simulated realistic infrared noise by combining various noise types, resulting in significant improvements. Additionally, researchers have addressed specific blur types, including motion blur [58 17]. out- of- blur [7I], and Gaussian blur that simulates atmospheric effects 662. These efforts have
5 2

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substantially enhanced image clarity and detail restoration in infrared imaging. TIR are also vulnerable other degradations, such as compression artifacts and low resolution. Several studies [ [6] 28 29] have tackled these challenges, leading to notable advancements. However, existing methods are typically constrained by specific degradation conditions, which significantly limits their generalization and effectiveness in real-world infrared image processing.
Table 1:
Illustration of our benchmark and existing infrared enhancement datasets. The "multiplica- tion" denotes the diverse camera viewpoints, including horizontal, surveillance, driving, etc.
Scene: @ Road
2: Square
3: City
@ Furest
@ Campus
@ Coastline
3: Residential Zone & Others
Corruption: I: Low Contrast
II: Blur
Stripe Nuise
Optical Noise
Gaussian Nuise
Dataset
Year
Format # of Images/Videst
Resolution
Camera angle
Scene
Corruption Type
EN
P3]
2019
Image
16
256x256
hurizuntal
(53-7
Iray 34
2021
Image
2000
256x192
horizontal
III
SBTI 25
2022
Vidco
4
640x480
horizontal& surveillae
(D3)
II
UIRD
20
2023
Video
17
640x512
borizontal&surrvilllaneee
(43
TIVID
2
2024
Video
518
320x256
horizontal
TV
HM-TIR (Ours)
2025
Image
1503
640x512
multiplication
(D~08)
I-V
All-in-One Image Restoration employs single model to address range of image degradation issues. PromptIR 4 and ProRes [39] use additional degradation context to introduce task information. IDR [6] explores the model optimization by ingredient-oriented clustering. AutoDIR [88 lever- ages latent diffusion with negradation-specine text embeddings to automate degradation handling. InstructIR [9] introduces natural language instructions to control restoration. However, most of these methods are only focus visible image enhancement, posing a chanllenge to apply in TIR images.
2.2 Thermal Image Enhancement Benchmarks
In recent years, several image enhancement benchmarks addressing specific degradations have been introduced, including the Iray Infrared Image Denoising dataset (34 and TIVID [2] for thermal image denoising, EN [23] for contrast enhancement, and SBTI 25 and UIRD 20 for deblurring. The Iray dataset comprises 2,000 pairs of real-world noisy infrared images captured indoors and outdoors alongside their clean counterparts. TIVID includes 518 diverse videos collected with a cooled infrared imaging system simulate various thermal infrared noises. EN contains 16 internet-sourced images designed to evaluate contrast enhancement. Additionally, SBTI consists of four videos captured on roads and around vehicles, while UIRD includes 17 videos generated through frame interpolation to produce more blurred images.
Table outlines the main attributes of these datasets, including scale, resolution, lighting conditions, and scenario types. Limited resolution, quality, and scenario, degradation types, and overall dataset size variety restrict their applicability for real-world infrared enhancement tasks.
3 Methodology
3.1 Problem Formulation
Infrared imaging systems, especially those using CMOS-based sensors, are prone to additional Fixed-Pattern Noise (FPN) alongside random noise types common in RGB imaging, such as Gaussian and lil-andd-eeperr noise. Additionally, unlike visible images, which contain detailed visual content and high-quality representation, infrared images capture only thermal distributions, making them particularly vulnerable to atmospheric conditions and temperature differences. As illustrated in Figure [] this degradation pipeline is affected by several factors, including low contrast due to minimal temperature differences, blurring from environmental radiation effects, and sensor-induced noise, which collectively reduce image clarity and quality. We categorize TIR degradation into three primary types: low contrast, blurring, and noise. The degradation process unfolds in a specific sequence: low contrast occurs first, followed by blurring, and ending with noise.
3

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For Counad
Minwee
Ganiaio Rive
Supatid Bleskor
Ki Malebigrabti
Back
Selecter Prog TreineghiP)
Regie Degraded
Etrentt Peppaated
Mav
Contuod
DSP:
Degrodatis spolts Prosps
TP: Typeg
Pururater spdak:
Figure 2: Schematic diagram of the proposed TIR enhancement framework. In subfigure (a), we first illustrate the TIR degradation process, including low contrast, blur, and noise across single and composite degradation scenarios. Subfigures (b) and (c) present details of the PPEN integrated with the image restoration model. Lastly, we depict our SPT in subfigure (d).
Therefore, given an observed clean image Le, the degraded image La can be formulated as:
La
n1
(Le) + Dr:
FPN
Blur
Low Contrast/
where C, K., Do. n,, and n, represent the degradation with low contrast, blur kernel, optics, stripe, and additive random noise, respectively. o denotes the composition operation.
As shown in Eq. ① TIR degradation encompasses multiple types that strongly impact TIR images. To enable the base enhancement model to address various degradations both complex composite and single degradation scenarios, we propose prompt fusion learning strategy, described in Sec. 3.22 Furthermore, to improve the model's stability in addressing composite degradations, we introduce the Selective Progressive Training strategy, as described in Sec. 3.3
3.2 Prompt Fusion Learning
The primary challenge in infrared image enhancement is the diverse range of degradation types, which single models cannot effectively address. Existing networks typically target specific degradations and struggle with complex composite ones. Although all-in-one restoration frameworks aim to remove multiple degradation types, they often falter with intricate composite degradations. To overcome this, we introduce the Progressive Prompt Fusion Network (PPFN). which enhances image restoration models for more effective infrared enhancement in complex scenarios. As shown in Figure ( and (c), our PPEN comprises dual-prompt processing module and a prompt fusion module.
In dual prompt processing, introduce sppe-specific and degradation-specifie prompts. The degradation-speecfie prompt Pang = {Pling Parg Pdeg) guides the model to adapt degradation types, while the type-specific prompt P Paype {Pryee: phype) is utilized enable the model to distinguish the difference between single and composite degradation scenarios. Here, n, b, and represent noise, blurring, and contrast degradation, respectively, while and h denote single and composite degradation scenarios. The degraded images processed by each step in either sin- gle or composite degradation scenarios with specific prompts Paagg Pdegs i E (n,, c} and Plyee E P types j {s, h). To extract prompt features, first obtain the degradation-specife prompt feature FP deg and type-specific prompt feature F Fl using two lightweight prompt encoders,

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Edeg and Etype. which are expressed as:
deg = Edeg Pdeg)) E {n, b, c), F. type Etype (Phype), y {s,
To represent the prompt more efficiently and guarantee subsequent injection being a conventional
modulation manner [27 22 prompt fusion module is introduced. Specifically, we concatenate the
two prompt features and then apply linear layer W/ fusion: followed by a non-linear activation 6(-), to obtain the final prompt feature F, p. as expressed below:
Fg (W usion (F Fr
ttpee))
(3)
deg'
where the operator Cat(.,) :) denotes concatenate operation. To integrate the prompt into the model's feature space and enable adaptability across degradation and scenario type, we calculate two channel- wise modulation parameters with suitable dimension, y and B, by applying linear layer Wp.
7.3 (F,)
Given the 1-th layer feature Fú Rhixw;x, in restoration model, with calculated modulation parameters " E R' RIxI Ixc and Br E IR 1x lxxe this adaptation process can be expressed as follows:
= Fi @ (1 + 7) + Bt.
(5)
where F, is the updated model feature that will be passed to the next model block. By integrating PPEN module, the model enables more effective handling of composite degradations.
f
hhi
Figure 3: Example images from our HM-TIR benchmark include: (a) skyscraper, (b) seaside, (c) mountain, (d) cross-sea bridge, (e) pendulum. (l) tower, (g) camping area, (h) commercial street, (i) mansion, (j) square, (k) Fer- ris wheel, (l) boats, and (m) tourist attraction. More examples are provided in the Supplementary Material A.3
3.3 Selective Progressive Training
To address the distinct challenges of composite and single degradations in TIR enhancement, we introduce the Selective Progressive Training (SPT) mechanism, as described Figure dd). SPT refines the degradation process by progressively enhancing each iteration through feedback loops. For composite degradations, where steps are applied sequentially, each iteration's 's output feeds into the next, enabling the model to learn and adapt to complex, interdependent degradation patterns. In contrast, for single degradations, where one type of degradation is present, standard training framework is employed. Given a degradation process with N steps, generate a sequence degraded images Ip = (1), 12 I by using clean image I, with corresponding to degradation-speeiff. prompts Pdng = {Pregg preg:"" ... -P J. As shown Figure 2 a). for single degradation scenario, each degraded image is generated by using specific degradation L., While for composite scenario, the k-th degraded image I is generated by specific degradation to k 1-th degraded image I/-1.
When training network, we set the initial input I = I because the N-th degraded image contains all degradations in composite scenarios. Then network removes each degradation step in reverse order, enhancing the degraded images accordingly. For the k-th iteration of degradation removal training, given the input image I the restored output image I/ is produced by the restoration model No. GT for this iteration of the restoration model defined as follows:
Ic.
for single scenario. for composite scenario.
1 9t
I
5

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This setup ensures that only the i-th specific degradation is removed for both single and composite scenarios. Then we calculate the model loss gradient VoL ge) but do not update the network parameters. This approach prevents the model from focusing excessively degradation while potentially neglecting others, and ensures that the training sequence does not interfere with single scenario training. For the next iteration's input, we use I directly in the composite scenario, the model will be affected by the removal of residual degradation from the previous iteration, leading to a significant drop in performance. To prevent this, we set the input I for the enhancement model in the next iteration (if it exists) as:
any single type of
for single scenario, sg( rest) for composite scenario,
where sg(-) denotes stop gradient operation to reduce training cost. After all iterations are completed, we update the model parameters using the sum of gradients computed across all iterations. In our TIR Enhancement setting, we define three-step degradation process: noise, blur, and contrast. In the training phase, the degradations are added sequentially for composition scenarios: noise, blurring, and contrast. In the inference phase, we reverse this order to progressively remove the degradation: denoising, deblurring, and decontrast. The procedure given Alg. T
3.4 High-quality Multi-scenarios TIR Benchmark
Considering that limited diverse data has hin- dered the development of TIR domain, we es- tablish a high-quality multi-scenario TIR bench- mark, HM-TIR. includes 1,503 TIR images encompassing various object types across dif- ferent scenarios, detailed in the last row of Table
Algorithm 1 Selective Progressive Training.
Require: Clean infrared images with {Ac}. a restora- tion Network No and other necessary hyper- parameters.
1: while not converged do
2: Generate In and Pdeg by randomly Prype: IN: for k N,...,11
I
Each TIR image has standard resolution of 640x512 and a wavelength range of 8 to 14 micrometers. To enhance thermal imaging per- formance by minimizing blur and increasing contrast, individually adjusted the focus for each scene and secured the settings with me- chanical tools before capturing. As shown in Figure 3 the HM-TIR benchmark includes a di- verse structured environments, such as skyscrap-
4:
5:
Thest = =T Sinn:Pee.. Ptype):
6:
Set GT image 17 according to Eq. (
7:
Calculate gradient VoL I
8:
Set next input I according Eq.
9:
end
10:
Update parameter 0 by gradient descent:
11:
end while
12:
return 0".
ers and Ferris wheels; unstructured settings like
forests; and challenging scenarios like densely populated areas and small targets. We also incorpo- rated various viewing angles, including aerial, eye-level, and low-angle. Additional data collection process and sensor equipment are provided in Supplementary Material
4 Experimental Results
4.1 Training Details
Training and testing data. In our experiments, we trained the TIR enhancement model on our HM-TIR dataset, which contains 1,503 TIR images encompassing diverse object types across var- ious scenarios. We divided the dataset into 80% for training and 20% for validation, ensuring a balanced evaluation of our model's performance. For multi-degradation TIR enhancement testing, we created two validation subsets to enable a more detailed assessment: the Normal Set and the Hard Set. The Normal Set comprises images with lower levels of degradation, whereas the Hard Set includes images with more severe degradation. For deggalaagoo TIR enhancement test- ing, applied the same settings as the Hard Set to create three separate single-degradation test subsets. The detailed degradation strategies and settings of degradation levels are provided in the Supplementary Material A.7
Training settings. We use Restormer 60 as the baseline model for TIR enhancement to evaluate our proposed module and strategy. All models are implemented in Py Torch on four 4090D GPUs with

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default settings. For the baseline model, we follow the Gated Degradation pipeline [65] to synthesize degradation, with the probabilities of all gates set to 0.8. During training, we adopt the LI loss [56] and employ the Adam optimizer with parameters 31 = 0.9 and / 0.999. Each model is trained with batch size of 4, using random cropping and flipping with a patch size of 256 x 256. The initial learning rate is set to 8 x 10-5 and decays to 10-6 following cosine annealing schedule. Each model is trained for a total of 300 epochs.
Evaluation metrics. In this work, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) [56] are employed to assess the quality of the enhancement results under reference- based conditions. The PSNR and SSIM assess the quality of results primarily from the spatial dimension, with larger values indicating better results. For reference-free conditions, three no-reference Image Quality Assessment (IQA) metrics to evaluate image quality: NIMA [50], MUSIQ 19 and NIQE [11. For NIMA and MUSIQ, higher values indicate better quality, while for NIQE, lower values are preferred.
4.2 Results on Multi-degradation TIR
To evaluate some TIR enhancement models, including WFAF 42). LRSID 4. and TSIRIE 44 as well as visible all-in-one restoration models such as DA-CLIP (37 and DIffUIR 70). we use the Normal Set to compare their TIR enhancement performance with approach. Quantitative and qualitative comparisons are shown in Figure 4
Infrared image
WFAF
LRSID
DA-CLIP
Ours
AA M
Al L.
ALW
Al L
Al yma
Infrared image
WEAF
RSID
STRIE
DA-CLIP
PSNR/SSIM
17.3608/0.388 47.2366
16.09-55/022000
15.0870/0.5832 1785
25.3227/0.81880
.y110000
Figure Quantitative and qualitative comparisons signal performance across competitive image enhancement methods and our proposed approach. The average PSNR and SSIM values in our Normal Set are provided below the comparison figures in blue.
TIR enhancement methods such as WFAF, LRSID, and TSIRIE exhibit lower PSNR and SSIM values because they are tailored for single degradations and struggle with complex composite scenarios. In contrast, DA-CLIP and DiffUIR, developed as all all-in-one enhancement methods for visible images, perform better; however, differences in imaging models between visible and infrared spectra lead to suboptimal results for infrared images. Our proposed method outperforms these approaches, achieving superior PSNR and SSIM scores and demonstrating enhanced signal restoration across multiple degradation scenarios. Qualitatively, traditional methods like WFAF, LRSID, and TSIRIE produce infrared images with substantial artifacts and background noise, while all-in-one approaches such as DA-CLIP and DifTUIR offer better restoration but still exhibit noticeable blurring and distortion. In contrast, our method excels at preserving critical structural information and fine details, reducing artifacts, and enhancing contrast.
We further evaluate our method alongside competitive approaches on the real-world Iray dataset [4 and adding an additional TIR enhancement method IE-CGAN [23]. Since it only provides the denoised result as ground truth, we use no-reference IQA metrics. Both quantitative and qualitative results are provided in Table @ and Figure 5) Existing TIR enhancement methods struggle with complex scenarios, typically addressing only one type of degradation. Furthermore, all-in-one enhancement methods designed for visible images are ineffective in handling the specific degradations
7

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Degraded
WFAF
LRSID
TSIRIE
IE-CGAN
DA-CLIP
DifFUIR
Ours
Figure 5: Qualitative comparisons of the competitive enhancement approaches and our method on Iray dataset.
Denoise
AP-BSN
CycleiSP
IDR
SDAP
Baseline
Ours
GT
Deblur
DeGANy2
MIMOUNet
FFTformer
Stripformer
Baseline
Ours
GT
Contrast
FFA-Nee
Figure 6: Qualitative comparison of single degradation between visible enhancement methods, baseline and our method three degradatggo test where "Baseline" refer to Restormer (60]
present infrared image processing. In contrast, our approach not only outperforms existing methods in IQA scores but also shows superior restoration capabilities in real-world degradation conditions.
Table Quantitative comparison Iray dataset. The best in red, the second-best blue. "I" means
lower value is better.
Metrics
Degraded
WFAF
LRSID
TSIRE
IE-CGAN
DA-CLIP
DiffUIR
Baseline
Ours
NIMA 3.5326
3.7321
3.5682
3.5359
3,4959
3.7004
3,5935
3.5812
3.8327 30.9072
MUSIQ
25.2459
25.1264
24.2095
23.7508
29.0350
27.7855
26,8066
27.7829
NIQEJ
10.1277
10.3536
8.6838
11.5204
9.4786
9.1896
9.3352
8.7776
8,4693
Due to page limitations, additional experimental results, e.g., more comparisons in our test set and real-world datasets, are provided Supplementary Material A.4
4.3 Results on Single-degradation TIR
To evaluate three TIR enhancement models with single degradation scenarios, we conduct experiments in test subsets with denoising, deblurring, and contrast enhancement.

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For denoising, we compare four state-of- of state-of-the-art approaches: AP-BSN (6).. CycleISP 599. IDR [66].
and SDAP 43]. For deblurring, we evaluate leading methods including D 24 MIMO- UNet g FFTformer [21], and Stripformer [52]. For contrast enhancement, treated as a combination
of haze and low-light enhancement, we include MSBDN [33 and FFA-Net 466 for dehazing,
alongside L.Former [53] and SCI [0] for low-light enhancement. Using three single-degradation subsets, we compared the performance of these methods relative to ours, with qualitative comparisons shown in Figure 6 While existing methods effectively reduce degradation, they still retain artifacts due to modeling differences between TIR and visible images, resulting in lower enhancement quality and fidelity. In contrast, method delivers superior performance in single-degradation TIR enhancement tasks, effectively reducing noise, recovering fine details, and enhancing contrast while preserving the natural appearance of images.
Table 3: Quantitative results comparing our method with five models in our two test sets, both with and without integration of PPEN module and SPT strategy. 'Average' refers the mean value across test sets. denotes methods using our approaches to train. Baseline and our approaches results are shown in and boxes, respectively. The best result is in red, and the second- best in blue.
Model
FocalNet PSNR/SSIM
PSNR/SSIM
FocalNet
PSNRISSIM
UFormer
UFormer! PENRISSIM
NAFNet PENRISSIM
PENRISSIM
NAFNeT XRestemer XRestormer PSNR/SSIM
PSNR/SSIMI
PSNR/SSIM
Resioreer
Restomeer PSNR/SSIM
Bridge
22.45/0 24.95/0.86 865 22. 22.75/0.850
24. 844 24 24.88/0.859 25.9 98/9888772 25.8200.899 25, 25.1470.890
24.28/0
24, 65/9.66
20.80N0.770 22.71/0.80 803 20.93/0. 780 20.95/0.775
19.87/0.758
22.4410.786
5200,77
23.52/0.812
21.12/0.785
22, 27.02000802
Leaning
22,61/0.843 26.44/0.86
24.5UND.833
25,95/0.88 847 26.39/0.849 27.2410.852
85/1865
29.60/0.877
27.8000.66. 863 28.46/1.879
Tower
22.89/0.801 21.21/0.806
18.82/0.723
17.72/0.724
23,25/0.778 23.49/0.806
23.29/0.800 807
23.65/0.829
23.10N0.825
23.23/0.832
89,8908885 24.33/0 851 23.20VD
25.71/0. 863 25.78/0.566 864 26 25.66000887 867 46.46//8774
27, 893
29.29/0.903 30.40/01908
25.07/0.836 24.86/0.88 839
23.21 70.7 25. 60000000000 839 21. 16/0.830
25.83/0.580 850
22.01/0.854
27.40/0.873
19/0.60.
28.50/0.882
Two
20:44/0.719 23.91/0.744 21.51/0.717 21,90/0.721
140. 22 22.05/0.735 24.79/0.740
24.13/0.744
25.40/0.750 25, 25.60007555
Skyscrapers
23.10/0.687
24.38/0.700
21.96/0.665
22.81/0.675
23. 16/0.667 22.57/0.693
24.57/0.696
26.05/0.709
24.71/0.701
26.08/0.716
Villa
25.37/0.798 24.98/0.805
22.2810.779 26.01M0.855
24.4900.782 23.23/0.773
25.17/0.809
23.66/0.817
23.58/0.767 25.37/0.797
100 27.7000.851
26.13/0.798
86/0884
25.71/0.808
29.06/0.865
25.36/0.806
22.46/0.785
27.26/0.816
29.73/1.871
Average
21.27/0.733 21.40/0.740
21.22/0.778 22.63/0.790
20.31/0.714 20.44/0.716 21,81/0.311 22.30N0.744
21.95/0.775 21.62/0. 768 22.29/0.776 23.74/0.792
23.4.8
541448801
23.06/0.758
24.75/18811
23.28/0.796 25.3220.818
22.87A1.557
23.27/0.764
4.4 Ablation studies
Validation of model architectures. In addition to Restormer, we evaluate our PPFN module with four other SOTA image enhancement models: NAFNet (6), UFormer (55), XRestormer (7]. and FocalNet [O]. We compare the performance of these five models with and without our PPFN module. Quantitative and qualitative results are presented in Table and Figure 7 respectively.
Degraded Infrored Emage
Ours
Normal
UForniee
NAFNeY
XReetornirr
PoralNec
Reetornier
-PPFN
PPPN
.PPPN
PPFN
-Prbs
Degraded Image
UFormez
Xisetormer
Hard
+PPFN
PPPEN
+PPEN
+PPEN
Figure 7: Visual comparison of five advanced methods with and without the integration of our approaches in Normal Set and Hard Set. Our method demonstrates superior visual quality and minimal error.
Quantitative and qualitative results show that all five baseline models exhibit lower PSNR and SSIM values and reduced enhancement quality on both Normal and Hard Sets, indicating their suboptimal performance with complex degradation. In contrast, integrating PPFN with each model consistently improves PSNR, SSIM, and TIR visual quality. Notably, our model achieves the best results, with an improvement of 8.76% the Normal Set in PSNR.
Study on prompt fusion learning. We train models with the same settings as in previous comparison experiments. Testing is performed the Hard Set, results are shown in Table 4 In dual prompt pro-

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Table 4: Ablation studies on the PPEN and SPT strat-
egy. The best is in red, and the second-best is in blue.
# of Prompt Prompt Fusion lter. PSNR
SSIM
22.8678
0.7568
22.6357 0.7524
DSP
23.1605 0.7635
TP/DSP
w/o non-linear
23.1487 0.7646
TP/DSP
Multiply
/
23. 1432 0.7627
TP/DSP
PPEN
14.5455 0.6125
Figure 8: Analyzing the enhanced images and error maps from each iteration. Zoomed and pseudo-color maps for the best view.
TP/DSP
PPEN
14.6080 0.6261
TP/DSP
PPEN
3. 23.2712 0.7643
cessing, applying degradation-speccfie and type/degradation- gains of 0.29 dB and 0..40 dB over the baseline, respectively. Regarding the prompt fusion strategy, removing non-line non-linear activation or replacing the concatenation operation with multiplication results in rapid PSNR decline, with the "Multiply" approach offering only minimal SSIM improvements. SPT reveals that directly applying iterative training to the baseline causes a PSNR drop of 0.23 dB.
prompts achieves performance
Analyzing the enhancement iteration. We demonstrate the enhanced images from each iteration along with corresponding PSNR and SSIM values, as shown in Figure g and Table respectively. Note that the iterations progress, specific degradations are incrementally removed, leading to gradual improvement in both PSNR and SSIM metrics.
TP: Single Degraded
TP: Composite Degraded
Niie- -Crdrrad
Nikee-Cntrast--Ee Bur Contradd Nike- Har
PSNRISSIM: 24.3800.62
PSNR/SSIM: 22.38
PSNR/SIM:: 232700751 PSNR/SSIME 226775 PSNRISSIME 16810661
Quantitative and qualitative comparison of TIR enhancement performance between different type- specific prompt setting in Normal Set.
Figure 9:
Quantitative and qualitative comparisons of our method with different order in each degradation removal process in Hard Set.
Figure 10:
Analyzing the prompt sensitivity. We evaluate the performance of our method with incorrect prompts and order, shown in Figure and Figure IO For incorrect prompts, we observe that using a single degradation scenario prompt with compositional degradation results in failure to remove degradation, with artifacts persisting. This indicates that the model struggles to eliminate residual degradation in each iteration under single scenario. For incorrect order, we demonstrate that the model exhibits lower performance and PSNR when the degradation removal order incorrect. This supports the hypothesis that optimal artifact removal occurs with fixed processing order. Our results highlight that the SPT strategy effectively handles fixed-order degradation removal, leading to improved performance.
5
Conclusion
This paper introduced new way for enhancing TIR images, managing complex degradation through dual-prompt processing and fusion modules. Our training scheme ensures robust performance across various scenarios. We also established comprehensive TIR benchmark for accurate evaluation. Experiments show that PPFN surpasses existing methods in clarity, detail preservation, and contrast enhancement, advancing TIR image enhancement for broader applications.
Acknowledgment
This work is partially supported by the National Natural Science Foundation of China NNos66230207 62372080, 62450072, U22B2052, 624B2033), the Distinguished Youth Funds of the Liaoning Natural Science Foundation 2No.2025JH6/101 100001). the Distinguished Young Scholars Funds of Dalian (N..2024RJ002) the China Postdoctoral Science Foundation (N0.2023M730741) and the Fundamental Research Funds for the Central Universities.
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A Supplementary Material
In Supplementary Material, present the degradation strategies and settings for degradation levels in Sec. A.I] Then we discuss the limitations of the current work in Sec. A.2 Also, we show additional
details on HM-TIR benchmark in A.3 Additionally, provide more experimental results in Sec. A.4 Finally, we provide a detailed discussions of our work in Sec. A.3
A.1
TIR Image Degradation Setting
In this subsection, we discuss the degradation simulation strategy and degradation level setting,
A.1.1 TIR Degradation Simulation Strategy
Low Contrast. The pixel output of the detector quantizes the radiation response. These values often unevenly distributed and confined to a narrow range due to small temperature differences [1]], resulting in low-contrast images. Also, the suboptimal transformation methods may fail map this limited range to a broader, visually distinguishable one, further reducing image interpret ability and utility. To simulate such degradation, we adopt simple method that adjusts the unevenly and narrow distribution of the input TIR images I, formulated as:
C(I) (I+ 3- MAX(I],
where 8, and MAX(T] denote the reduction factor, offset factor, and the possible max pixel value of the image, respectively.
Blur. TIR images often suffer from blurring degradation due to various factors inherent to cameras and their surrounding environments. Two common types of blur in TIR images are low-pass blur and motion blur. Considering the availability of only single-frame TIR images, we focus exclusively on low-pass blur for simplicity.
Low-pass blur arises from atmospheric turbulence effects and the inherent limitations of camera capabilities, leading loss of image details and reduced quality. To simulate this degradation, follow prior works 722 and utilize an isotropic Gaussian blur and randomly select the kernel size and standard deviation of the kernel k and blur the input TIR image, expressed
K[II = k I.
(9)
where "s" denotes the convolution operation.
Noise. In TIR image processing, there are two types of noise mainly encountered: Fixed-Pattern Noise (FPN) and Random Noise.
FPN refers to the unique noise pattern characteristic each digital camera. This phenomenon commonly arises when the camera uncalibrated or affected by internal temperature fluctuations, and it particularly pronounced in long-exposure shots. In TIR imaging systems, the most common types of FPN are Stripe Noise and Optics Noise.
Stripe Noise a prevalent issue in TIR image processing, primarily resulting from amplification variations across the one-dimensional detector arrays in CMOS-based cameras [14). Even after calibration, internal temperature fluctuations can exacerbate this phenomenon. This type of noise typically manifests uneven horizontal or vertical stripe patterns. We assume the gain and offset of each detector unit to be approximately 1 and 0, respectively, and model them as two zero-mean Gaussian distributions with standard variances og and o., respectively. To simulate degradation in TIR images, we randomly apply gain and offset along a single dimension, expressed as:
n, (I) (1 + I
(10)
where and represent gain and offset, sampled from two zero-mean Gaussian distributions, respectively.
Optics Noise arises from temperature response inconsistencies in TIR camera detector units. Pro- longed use leads to non-uniform optics noise, influenced by internal temperature changes [3. Two scenarios typically occur: during heating, the image center darkens while the edges brighten; during cooling, the center brightens while the edges darken. Following prior work [47), model optics noise as a quartic cosine function of the distance between any pixel and the image center.
n2 I I S, cosa ((p.p)))
(11)
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Structuresf
Figure 11: Additional example images from our HM-TIR benchmark, including: (a) structured environment, (b) unstructured settings, (c) challenging scenarios.
where So. and represent the strength factor, current pixel position, and the center pixel position, respectively. r(p. Pe) denotes the normalized distance, defined as:
(p.
(() = max (axx(ds((ppPP)
(12)
where dist(:.-) represents the distance function. We use the Euclidean distance to compute the separation between two points.
Random Noise is the result of several factors in TIR sensor sampling, egg. read noise and dark current in camera circuit [15]. It is generated by various factors and manifests high-frequency random noise, which is not constant and can be described by statistical distributions. We utilize
adding white Gaussian noise n, sampled from Gaussian distribution with zero mean and standard variance [30].
For noise addition order, we first consider optics noise as it arises during the thermal radiation signal collection stage. Next, stripe noise n, is introduced, as it occurs during the sensor production stage. Finally, additive Gaussian noise n, is added the last stage to represent random noise.
A.1.2 TIR Degradation Level Settings
For degradation level settings, the simulator controlled by eight parameters, enabling the generation of various types of degradation, including low contrast, blurring, and noise. For low contrast, the reduction factor
set to range from 0.4 to 0.8, and the offset factor /3 is set from 0.1 to 0.2 for the Normal Set. For the Hard Set, ranges from 0.2 to 0.8, and ,3 ranges from 0.2 to 0.4. For blurring, the kernel size is varied from 7 to 17 and the standard deviation from I to 2 for the Normal Set, whereas for the Hard Set, the kernel size is adjusted from 7 to 23 and the standard deviation from to For noise, the standard deviation of gain 0g ranges from 0.03 to 0.07, the offset a varies from to 3, the strength factor s. ranges from 15 to 55, and the standard deviation of white noise o, ranges from 5 to 15 for the Normal Set. For the Hard Set, og varies from 0.03 to 0.10. a ranges from to 5,
so spans from 15 to 75, and the standard deviation of white noise o, ranges from 5 to 20.
A.2 Limitations
Due to the inherent challenges associated with capturing paired degraded and clean TIR images, the degradation model employed in this study may not fully replicate the complexities of real-world degradation processes. The TIR imaging processing typical susceptible complex composite distortions, including motion artifacts, radiation attenuation, diffraction effects, and sensor-induced noise (64). However, as demonstrated in Figure 5 14 and Table 2 the proposed method achieves commendable performance in real-world TIR enhancement evaluations. These results provide strong validation for our degradation modeling strategy and support the effectiveness of the model under realistic conditions.
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To overcome the limitation of the current approach, future work will aim to develop more compre- hensive degradation model that incorporates broader range of noise and distortion types. This will improve generalization of TIR enhancement model, enabling more accurate real-world applications.
Table 5: Complexity comparison parameters, FL.OPs, and time.
Methods
TSIRIE
DA-CLIP
DiffUIR
NAFNet
XRestformer
Baseline
Ours
Params(M)
2.52
233.14
12.41
17.06
25.98
26.09
26.60
Fl)
77.91
660.18
164.68
79.47
820.52
704.10
704.33
Time(s)
0.01
17.07
0.325
0.024
0.348
0.292
0.876
A.3 Additional Details of Benchmark
We have presented some examples of our HM-TIR benchmark the main paper. this section, we show additional examples of our benchmark in Figure These HM-TIR benchmark example images include diverse conditions, such as structured environments, unstructured settings, and challenging scenarios. This highlights the high quality of our benchmark, which offers multi-scenario coverage and incorporates a diverse range of real-world challenges.
For the designed TIR prototype, we developed TIR calibration board/algorithms to eliminate systematic errors/noise, and then reinforced the assembly to reduce environmental vibrations and added protective shields against electromagnetic interference. Besides, focus was adjusted for each scene, and post-processing included strict quality checks. In the post-processing stage, each image underwent strict quality checks to ensure reliability and high-quality. For non-cooled passive TIR sensor, we customized is wavelength range of 8 14pm, an aperture f/1.2, HV-FOV of 48 x38" Res.640 x 512, and manual focus adjustment capabilities.
A.4 More Experiments
We provided some experiment results our method test set and the Iray dataset in the main paper. In this section, we show the additional experiments. Firstly, demonstrate more visual comparisons on the Normal Set and real-world Iray dataset, shown in Figure T3] and Figure 14] These results further show that our approach has superior enhancement capabil- ities in our simulation scenarios and real-world degradation scenarios.
Figure 12: Comparison PSNR and SSIM value curves during training across five models, with and with- out our approaches.
Then, we demonstrate that the PSNR and SSIM curves during training, shown in Figure 12 indicate that all five models achieve higher performance upon training completion with our PPFN module. This result demonstrates that our module and strategy adapt effectively, consistently enhancing the visual performance of each model.
In addition, we show the visual comparison of three basclines and their results with our strategies in TNO (51) and Roadscene [57), two real-world degraded TIR datasets, as shown in Figure 15 Figure 16, respectively. It can be seen that the three baselines demonstrate limited enhancement performance, primarily reducing noise. In contrast, with our PPFN, models can generate more detailed outputs, reduce noise, and effectively enhance contrast.
and and
Finally, we conduct complexity comparisons, as shown in Table 5 For fair evaluation, All the the models are equipped in hardware environment with a NVIDIA RTX 4090 GPU with 24GB memory and the input TIR image resolution is 640x512. Our method, while introducing additional parameters and inference time due to prompt processing and stepwise degradation removal, outperforms baseline approaches in handling such complexities.
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A.5
Discussions
A.5.1 TIR Degradation Simulation Pipeline
The proposed TIR degradation simulation follows a fixed order. In contrast, some high-order degradation models have been extensively explored in the RGB domain, such as [54] and [63]. realistic blur, noise, and compression artifacts are typically simulated by randomly or repeatedly applying multiple degradation operations, mimicking the effects of camera imaging, image editing, and Internet transmission. However, these simulation pipelines are inherently tailored to natural RGB images and fail to capture modality-specifee degradations in TIR imaging, which are predominantly associated with the camera sensing process. Unlike RGB imaging, which relies on reflected light and sensitive to illumination and weather, TIR imaging captures emitted thermal radiation and remains stable under varying conditions. However, due its longer wavelength and sensor characteristics, TIR images suffer from unique degradations such as stripe noise, optical noise, and radiation-caus caaooodd low contrast, especially uncooled CMOS-based systems. These structured and composited degradations uncommon in RGB images and cannot be effectively handled by RGB-oriente models.
A.5.2 Difference Between Cascade Multiple Specific Networks
Traditional methods, such as Cascade Multiple Specific Networks, address composited degradation by employing multiple independent sub-networks. In contrast, although our method performs iterative processing, does not cascade multiple independent networks. Instead, it employs a unified network across iterations, modulated by degradation and scenario prompts, enabling progressive removal of each degradation type in different scenarios. This design avoids structural redundancy, and all iterations reuse the same network parameters, with only lightweight prompt modules introduced. While iterative inference may require more steps than singlc-pass baselines, the added cost is slight and results in significantly improved performance.
A.5.3 Prompt Design Detail
In our framework, two types of prompts are used as conditional inputs to guide the network. Follow- ing [22]. these prompts randomly initialized and fixed for each prompt type. During training, they gradually encode task-relevant conditions through model optimization under specific prompts. We manually define the prompt corresponding specific degradation conditions. Specifically, the prompts are designed to represent the degradation type and processing scenario. As part of future work, we plan to explore more flexible and scalable prompt designs. In particular, will investigate the use of image-based self- self- aef-prompting mechanisms, where the model dynamically generates degradation-awar prompts from the input itself. Additionally, we are interested integrating language- based prompts to express complex degradation descriptions in more interpretable and contoolnteol manner.
!!II
!!II
!!II
Degraded
WEAF
LRSID
TSIRIE
DA-CLIP
DIIUIR
Ours
GT
Figure 13: Additional visual comparisons of our method with other competitive approaches on our Normal Set.
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Degraded
WFAF
LRSID
TSIRIE
IE-CGAN
DA-CLIP
DifUUR
Ours
Figure 14: Additional visual comparisons our method other competitive approaches on Iray dataset.
-P1N
.P7TX
4027X
-PHIX
SATIE APTEN
Xiol FITT
Figure 15: Visual comparisons of three baselines and with our PPFN approach in TNO dataset.
Dun
KAPSa
Dum
KATMI
Figure 16: Visual comparisons three haselines and with our PPFN approach Roadscene dataset.
26


Reviews:
[{"review_id": "MFP59ptswN", "reviewer": "Reviewer_3U2T", "summary": "The 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.", "questions": "(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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \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?", "was_revised": true, "final_justification": "Thank you to the author for your detailed reply, which effectively answered my questions. After considering the opinions of other reviewers, I have decided to increase my score.", "rebuttal": "## **Rebuttal to Reviewer 3U2T**\n\nWe thank the reviewer for their positive feedback and for recognizing the effectiveness of our proposed framework in addressing complex degradations in TIR images. Below, we provide detailed responses to the concerns regarding degradation order, noise handling, prompt design, and TIR-RGB differences.\n\n### 1. **Fixed Degradation Order Assumption (W1):**\n\n**We argue that in TIR imaging, degradations typically follow a consistent order driven by imaging processes.** Low contrast comes first from limited thermal radiation, followed by blur from defocus or turbulence, and finally hybrid noise such as fixed pattern noise and random noise. Our framework follows this order, and we evaluate on real-world TIR images, **as shown in Figure 5 and Table 2, this design leads to strong performance in real-world scenes.** To test robustness, we also evaluated the model under incorrect degradation removal orders. As shown in Figure 10, performance drops significantly in some case, confirming the model works with a fixed processing order. In future work, we plan to model and predict degradation order to handle more complex scenarios.\n\n### 2. **Handling Simultaneous Multiple Noise Types in Infrared Images (W2) :**\n\nRegarding diverse noise patterns, we observe that stripe noise and optical distortion often coexist in real-world thermal infrared images due to the radiation inside the imaging body and environmental temperature.[1] **Consequently, rather than handling each type of noise independently, we consider their coexistence as a unified degradation pattern and model it as hybrid noise (see Figure 2(a), Noise Part).** A degradation prompt is employed to guide the model in conditional learning to suppress this pattern during both training and inference. The specific degradation simulation formulations are provided in Supplementary Material A1.1.1.\n\n### 3. **Ambiguity in Prompt Design (W3) :**\n\nWe acknowledge that the current manuscript lacks a clear explanation of how the prompts are defined, which may have caused confusion. In our framework, **two types of prompts are used as conditional inputs to guide the network. These prompts are randomly initialized and fixed for each prompt type, and during training,** they gradually encode task-relevant conditions through model optimization under specific prompts. **We manually define the prompt corresponding specific degradation conditions.** Specifically, the prompts are designed to represent the degradation type and processing scenario. As part of future work, we plan to explore more flexible and scalable prompt designs. In particular, we will investigate the use of image-based self-prompting mechanisms, where the model dynamically generates degradation-aware prompts from the input itself. Additionally, we are interested in integrating language-based prompts to express complex degradation descriptions in a more interpretable and user-controllable manner.\n\n### 4. **Differences Between TIR and RGB Imaging and Artifact Causes (W4):**\n\n**Unlike RGB imaging, which relies on reflected light and is sensitive to illumination and weather, TIR imaging captures emitted thermal radiation and remains stable under varying conditions.** However, due to its longer wavelength and sensor characteristics, **TIR images suffer from unique degradations such as stripe noise, optical noise, and radiation-caused low contrast, especially in uncooled CMOS-based systems.[1-2]** These structured and composite degradations are uncommon in RGB images and cannot be effectively handled by RGB-oriented models, **which typically focus on random noise, bad weather,  or low illumination and often fail under TIR scenarios, as shown in Figure 4 and 5.** Moreover, modeling all degradations within a single network still yields unsatisfactory results when dealing with complex, dynamic, and compound degradations, as shown in Figure 7 and Table 3.  Consequently, we propose a TIR-specific framework that explicitly and decomposed models thermal degradations using prompt-based learning. Our method integrates a prompt fusion network and sequential tuning strategy to effectively handle both single and composite degradations, demonstrating robust performance where RGB-based models fall short, as demonstrated in Figure 4, Figure 5, and Table 2.\n\n---\n\n[1] Cai et al. Exploring video denoising in thermal infrared imaging: physics-inspired noise generator, dataset and model. IEEE TIP (2024).\n\n[2] Danaci and Akagunduz. A survey on infrared image & video sets. Multimedia Tools and Applications (2024)."}, {"review_id": "xuVFAPdjBt", "reviewer": "Reviewer_RuCv", "summary": "This 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.", "questions": "(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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I appreciate the author’s rebuttal, which has satisfactorily resolved my concerns. Accordingly, I have decided to increase my score.", "rebuttal": "## **Rebuttal to Reviewer RuCv**\n\nWe sincerely thank the reviewer for the positive feedback and for recognizing the novelty of our method, the benchmark construction, and the clarity of our presentation. We address the concerns in detail below.\n\n### 1. **Lack of Infrared-Specific Design (W1, Q1):**\n\n**We argue that our framework is specifically designed to address the complex degradations found in thermal infrared (TIR) images by adopting general prompt-based techniques.** These degradations, including varieties of fix pattern noises and low contrast, **are fundamentally different from those in RGB images and have been insufficiently explored in existing literature.** Prior methods often assume simple or isolated degradations, while our method **introduces a progressive prompt fusion network that enables progressive removal in composite degradation patterns or directly handle single degradation scenario.** In addition, we establish **a dedicated TIR benchmark (HM-TIR) and design a selective progressive training strategy to improve robustness across varying degradation scenarios.** While our approach can apply more broad domain in principle, it provides a targeted solution to a domain-specific problem that is not well addressed by existing models.\n\n### 2. **Insufficient Comparison with Existing Methods (W2):**\n\nThe key different is that, instead of modeling all degradations jointly within a single network, focusing on specific degradation, or using existing all-in-one approaches that address multiple single degradations, **our method employs a prompt-guided framework with a unified backbone and fusion of degradation and scenario-specific prompts.** This design enables explicit and progressively handling of diverse degradations across different scenarios. For comparison, our manuscript includes extensive evaluations demonstrating the effectiveness of our method. For example, the Figure 4 shows our clear advantage over TIR-specific and RGB all-in-one models on synthetic composite data. Figure 5 and Table 2 extend this to real-world TIR images, where our method consistently achieves better visual and metric results. Figure 7 and Table 3 compare baselines with and without our prompt fusion module. These comparisons demonstrate the novelty of our model over TIR-specific methods, RGB all-in-one models, and baseline variants.\n\n### 3. **Architectural Complexity and Redundancy (W3, Q2):**\n\nAlthough our method performs iterative processing, it does not cascade multiple independent networks. **Instead, it employs a unified backbone across iterations, modulated by degradation and scenario prompts, enabling progressive removal of each degradation type in different scenarios.** This design avoids structural redundancy, and all iterations reuse the same network parameters, with only lightweight prompt modules introduced. While iterative inference may require more steps than single-pass baselines, the added cost is slight and results in significantly improved performance, as demonstrated in Figure 7, Table 3, and Table 5.\n\n### 4. **Limited Generalization to Unknown Degradations (W4, Q4):**\n\nWe acknowledge that our current framework is based on a predefined set of degradation types commonly observed in TIR images, which may limit its ability to handle unknown degradations. **However, it is important to note that we have already evaluated our method on real-world TIR data (Figure 5, Table 2), demonstrating its strong practical effectiveness.** Additional experiments on other real-world degradations are provided in the supplementary material, primarily to demonstrate that our strategy can effectively enhance TIR images in broader real-world scenarios. **The results further support the effectiveness and generalization capability of our method, with consistent visual improvements in noise reduction, contrast enhancement, and structure preservation.** Given the already significant visual improvements and performance gains, we believe the **current results sufficiently validate the robustness and practicality of our approach.** While our current setup enables effective TIR enhancement, we agree that improving generalization to unknown degradations or adapting to newly emerging degradation types remains a critical and promising direction for future work.\n\n### 5. **Ambiguity in Prompt Design (Q3):**\n\nWe acknowledge that the current manuscript lacks a clear explanation of how the prompts are defined, which may have caused confusion. In our framework, **two types of prompts are used as conditional inputs to guide the network. These prompts are randomly initialized and fixed for each prompt type, and during training,** they gradually encode task-relevant conditions through model optimization under specific prompts. **We manually define the prompt corresponding specific degradation conditions.** Specifically, the prompts are designed to represent the degradation type and processing scenario. As part of future work, we plan to explore more flexible and scalable prompt designs. In particular, we will investigate the use of image-based self-prompting mechanisms, where the model dynamically generates degradation-aware prompts from the input itself. Additionally, we are interested in integrating language-based prompts to express complex degradation descriptions in a more interpretable and user-controllable manner."}, {"review_id": "jeiSpE6JjX", "reviewer": "Reviewer_VhxR", "summary": "This 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.", "questions": "Please 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?", "limitations": "No.  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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\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).", "was_revised": true, "final_justification": "The authors have already responded to my concerns.", "rebuttal": "## **Rebuttal to Reviewer VhxR**\n\nWe sincerely thank the reviewer for their thorough review and constructive feedback. Below we address each of the raised concerns in detail.\n\n### 1. **Clarification on the hybrid degradation in TIR enhancement (W1)**\n\nWe thank the reviewer for this important observation. We agree that several prior works (e.g., [1-3]) have explored thermal infrared (TIR) image enhancement under certain hybrid degradation settings. For exmaple, [1] address stripe and optical noise, [2] focus on contrast and detail enhancement, and [3] target video denoising with a physics-inspired composite noise generator. **These methods typically operate under specific degradation setting, lack comprehensive degradation modeling,** and are not designed to generalize across diverse and dynamically composite degradation domains. While our work is the first to **systematically simulate, benchmark, and address comprehensive, real-world composite degradations in TIR images.** \nWe will revise the manuscript to clarify our novelty and include a more detailed comparison with [1-3] in the Related Work section.\n\n### 2. **Dataset type not clarified (W2)**\n\nWe thank the reviewer for this valuable suggestion. To improve clarity, we will revise Table 1 by adding an additional row labeled “Input Type,” indicating whether each dataset is based on single images or video frames. For reference, **EN, Iray, and our proposed dataset are based on single images, while SBTI, UIRD, and TIVID are constructed from video sequences.**\n\n### 3. **Readability issues (W3)**\n\nWe appreciate the reviewer pointing this out. We will increase the font size of figure and table entries in the revised version to ensure that visualizations maintain clarity when printed or viewed digitally.\n\n### 4. **Fixed Degradation Order Assumption (Q1)**\n\n**We argue that in TIR imaging, degradations typically follow a consistent order driven by imaging processes.** Low contrast comes first from limited thermal radiation, followed by blur from defocus or turbulence, and finally hybrid noise such as fixed pattern noise and random noise. Our framework follows this order, and we evaluate on real-world TIR images, **as shown in Figure 5 and Table 2, this design leads to strong performance in real-world scenes.** To test robustness, we also evaluated the model under incorrect degradation removal orders. As shown in Figure 10, performance drops significantly in some case, confirming the model works with a fixed processing order. In future work, we plan to model and predict degradation order to handle more complex scenarios.\n\n### 5. **Ambiguity in “multiplication” notation (Q2)**\n\nThank you for pointing out this ambiguity. The “multiplication” format is used to denote **diverse camera viewpoints, including variations across horizontal angles, surveillance views, and driving perspectives.** We will revise the table and its caption to clarify this interpretation explicitly.\n\n### 6. **Coverage of extreme weather conditions (Limitation)**\n\nIn Limitations section, we admit that our current degradation model does not fully cover all possible TIR degradations. Although we did not consider the extreme weather conditions, **we believe our framework retains some degree of generalization to such scenarios.** Specifically, in rainy or foggy environments, the increased scattering and absorption of thermal radiation reduce the energy reaching the sensor, especially from low-emissivity or distant objects. **This results in compressed temperature distributions and weaker radiation intensity, manifesting as low contrast.** Since our framework is trained to handle such contrast-degraded scenarios, it can partially generalize to these extreme weather-induced degradations, even without direct exposure during training. **Consequently, although our current model does not explicitly simulate all atmospheric effects like rain or fog.** We will clarify this point in the revised manuscript to highlight the potential of our approach to generalize beyond the explicitly simulated degradations."}, {"review_id": "f9ZyvKSSsP", "reviewer": "Reviewer_anvK", "summary": "This 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.", "questions": "Please refer to the weakness.", "limitations": "Yes.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thanks to the authors for their responses. The authors' rebuttal addressed my questions with a lot of efforts. In a such multifaceted complex degradation simulation, training models with TIR images and then working well in real-world is a huge advancement. Overall, this work is quite good and has laid the foundation for the following work.", "rebuttal": "## **Rebuttal to Reviewer anvK**\n\nWe sincerely thank the reviewers for recognizing the novelty and significance of our work. Below, we provide clarifications and responses to address the main concerns.\n\n### 1. **Clarification on the Difference Degradations Between TIR and RGB (W1):**\nWe thank the reviewer for raising this important point. While high-order degradation models have been extensively studied in the RGB domain, thermal infrared (TIR) imaging exhibits fundamentally different physical characteristics and noise properties due to its reliance on thermal radiation rather than visible light. For RGB-based degradation models such as [1] and [2], realistic blur, noise, and compression artifacts are simulated by applying multiple degradation operations in a specific or random order, reflecting the effects of processes such as camera imaging, image editing, and Internet transmission. However, **these models are inherently designed for the general image and fail to capture the modality-specific degradations present in TIR images**, which mainly include the camera imaging. Specifically, **unlike RGB imaging, which relies on reflected light and is sensitive to illumination and weather, TIR imaging captures emitted thermal radiation and remains stable under varying conditions.** However, due to its longer wavelength and sensor characteristics, **TIR images suffer from unique degradations such as stripe noise, optical noise, and radiation-caused low contrast, especially in uncooled CMOS-based systems.** These structured and composite degradations are uncommon in RGB images and cannot be effectively handled by RGB-oriented models, **which typically focus on random noise, bad weather,  or low illumination and often fail under TIR scenarios, as shown in Figure 4 and 5.** We will revise the manuscript to incorporate these RGB high-order degradation works.\n\n### 2. **Advanced Recent All-in-one Frameworks (W2):**\n\nRecent unified frameworks such as PromptIR [3], AutoDIR [4], and InstructIR [5] adopt various strategies for general image restoration: PromptIR uses implicit prompt tokens to improve the network in multi-task setting; AutoDIR leverages latent diffusion with degradation-specific text embeddings to automate degradation handling; InstructIR introduces natural language instructions to control restoration. While effective in the RGB domain, these methods still mainly focus on multiple single degradations, which are not designed for the composite, domain-specific degradations found in TIR images. **While our method employs a prompt-guided framework with a unified backbone and fusion of degradation and scenario-specific prompts.** This design enables explicit and progressively handling of diverse degradations across different scenarios. These all-in-one models will be incorporated into the revised version of the manuscript.\n\n### 3. **Clarification of Ambiguous Representations and Figures (W3):**\n\nWe acknowledge that certain notations and figures may have caused confusion. Specifically, the “multiplication” format is used to denote **diverse camera viewpoints, including variations across horizontal angles, surveillance views, and driving perspectives.** Additionally, we will increase the font size and enhanced the resolution of Figure 6 to improve readability.\n\n### 4. **Supplementing Cost Comparison with Critical Implementation Details (W4):**\n\nTo ensure fair and reproducible comparisons, we have supplemented the cost analysis section with detailed implementation information, including the hardware environment (NVIDIA RTX 4090 D GPU with 24GB memory) and the input TIR image resolution (640×512) used during inference."}]

Author rebuttal:

Meta-review: The paper introduces the Progressive Prompt Fusion Network (PPFN) and a Selective Progressive Training (SPT) mechanism to enhance thermal infrared (TIR) images under complex and composite degradation scenarios. 

Key innovations include:

•	Dual prompts (type-specific and degradation-specific) to identify and decouple degradations.

•	A plug-and-play fusion block to integrate prompts into the learning process.

•	A progressive training strategy to iteratively address multiple degradation types.

•	Construction of a high-quality, multi-scene TIR dataset for benchmarking.

Strengths highlighted by reviewers:

•	Novelty and effectiveness in handling hybrid degradations.

•	Superior performance over state-of-the-art methods in both quantitative and qualitative evaluations.

•	Robustness and generalization in real-world scenarios.

•	Clear and satisfactory rebuttal that addressed reviewers' concerns, leading to score increases from multiple reviewers.
Decision: Accept (poster)