Title: Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing
Venue: NeurIPS 2024
Accepted: yes
Keywords: Polarimetric Imaging

Exposure fusion

Deep Learning
Abstract: Polarimetric imaging is a challenging problem in the field of polarization-based vision, since setting a short exposure time reduces the signal-to-noise ratio, making the degree of polarization (DoP) and the angle of polarization (AoP) severely degenerated, while if setting a relatively long exposure time, the DoP and AoP would tend to be over-smoothed due to the frequently-occurring motion blur. This work proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. By adopting a neural network-based three-phase fusing scheme with specially-designed modules tailored to each phase, our framework can not only improve the image quality but also preserve the polarization properties. Experimental results show that our framework achieves state-of-the-art performance.

Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing

Abstract

Polarimetric imaging is a challenging problem in the field of polarization-based vision, since setting a short exposure time reduces the signal-to-noise ratio, making the degree of polarization (DoP) and the angle of polarization (AoP) severely degenerated, while if setting a relatively long exposure time, the DoP and AoP would tend to be over-smoothed due to the frequently-occurring motion blur. This work proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. By adopting a neural network-based three-phase fusing scheme with speciallydesigned modules tailored to each phase, our framework can not only improve the image quality but also preserve the polarization properties. Experimental results show that our framework achieves state-of-the-art performance.

1 Introduction

Polarimetric imaging aims to obtain the degree of polarization (DoP) and the angle of polarization (AoP) of the scene to provide physical clues for downstream polarization-based vision applications ( e.g ., reflection removal [16], shape from polarization [5], dehazing [31], etc .). In practice, the DoP and AoP cannot be captured directly, but are usually calculated from polarized snapshots 1 in an indirect manner. However, since a polarizer would block part of the light, selecting an appropriate exposure time could be challenging, making the captured polarized snapshots often degrade [15, 33]. As shown in Fig. 1 (left), setting a short exposure time would result in a very low signal-to-noise ratio (SNR), making the DoP and AoP severely degenerated; while if setting a relatively long exposure time to increase the SNR, motion blur is more likely to occur, making the DoP and AoP over-smoothed, as shown in Fig. 1 (middle). To deal with the above issues, several methods have been proposed to handle the low-light noise [10, 25, 15, 32] or motion blur [33] in the polarized images. Since these methods can work in a polarization-aware manner ( i.e ., they explicitly take the preservation of polarization properties into consideration), they usually demonstrate higher performance compared with the corresponding methods designed for conventional images [3, 29, 14, 23]. However, due to the ill-posedness of the problems they face, the quality of their results is still limited.

Considering the fact that different types of degraded polarized snapshots would provide complementary knowledge, i.e ., the short-exposure noisy ones tend to be clear while the long-exposure blurry

0

°

90

°

45

°

135

°

DoP

AoP

0

°

90

°

45

°

135

°

DoP

AoP

0

°

90

°

45

°

135

°

DoP

AoP

Noisy (short-exposure)

Blurry (long-exposure)

Clean and clear (fused)

Complementarily fusing

emr

ones tend to be clean, an intuitive strategy to improve the quality of polarimetric imaging could be complementarily fusing a degraded pair of noisy and blurry polarized snapshots. Such a strategy can not only achieve an effect similar to 'boosting' ( i.e ., combining multiple weak ones into a strong ones) to produce clean and clear results, but also bring the existing degraded polarized snapshots alive. However, current methods that can fuse noisy and blurry pairs [17, 2, 27, 28] are designed for conventional images, which are not suitable for polarimetric imaging since they cannot preserve the polarization properties, resulting in inaccurate DoP and AoP.

In this paper, we propose a quality-improved and property-preserved polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones, as shown in Fig. 1 (right). Specifically, we design a neural network-based three-phase fusing scheme that can explicitly take the preservation of polarization properties into consideration. The first phase is irradiance restoration , aiming to restore the polarization-unrelated high-level irradiance information of the scene by recovering the total intensity of the light, where the color and structure cue fusion (CSCF) module is proposed to make full use of the color and structure cues encoded in the Stokes parameters. The second phase is polarization reconstruction , aiming to establish the physical correlation between the polarized images by reconstructing the DoP and AoP, where the coherence-aware aggregation (CAG) and coherence injection (CI) modules are proposed to optimize the values of the DoP and AoP in a Cartesian coordinate representation. The third phase is artifact suppression , aiming to suppress the artifacts lying in the details by performing refinement in the image domain. Unlike the fusing methods designed for conventional images [17, 2, 27, 28], our framework can fully utilize the complementary knowledge from the noisy and blurry pairs in a polarization-aware manner, by virtue of our three-phase fusing scheme. Different from the polarized image enhancement methods [32, 33] designed for enhancing a single noisy or blurry polarized snapshot, our framework can effectively explore the usage of different physical quantities to improve the overall performance, thanks to the specially-designed modules tailored to each phase. To summarize, this paper makes contributions by demonstrating:

A quality-improved and property-preserved polarimetric imaging framework, for the first time applying a fusing strategy to polarimetric imaging.

A neural network-based three-phase fusing scheme, fully utilizing the complementary knowledge from the noisy and blurry pairs in a polarization-aware manner.

Specially-designed modules tailored to each phase, effectively exploring the usage of different physical quantities to improve the overall performance.

2 Related work

Low-light enhancement and deblurring for polarized images. There are many methods specially designed for enhancing polarized images, aiming to improve the quality of polarimetric imaging.

IPLNet [10] and ColorPolarNet [25] adopted residual dense blocks to build the backbone for dealing with multiple polarized low-light noisy images simultaneously. Li et al . [15] proposed a noise modeling method for realistic polarized low-light data synthesis along with a powerful vision Transformer-based network structure to reduce the noise. PLIE [32] designed a novel Stokes-domain low-light enhancement strategy and proposed a dual-branch network to reduce the artifacts lying in the DoP and AoP. PolDeblur [33] proposed a polarized image deblurring pipeline along with a two-stage network to remove the motion blur in a polarization-aware manner. However, they are not good at recovering details due to the ill-posedness of the problem they face.

Image enhancement by fusing noisy and blurry pairs. In comparison with the image enhancement methods that only take a single degenerated image as the input ( e.g ., low-light enhancement [3, 29] or deblurring [14, 23] methods that focus on processing either a single low-light noisy image or a single blurry image), fusing noisy and blurry pairs could usually achieve higher performance and better generalization ability since additional information can be acquired. Early works are usually based on numerical optimization. Yuan et al . [26] adopted a residual deconvolution process along with a gain-controlled deconvolution process to reduce the overall ringing artifacts during fusing. Choi et al . [4] designed a novel camera system that could capture two blurry images and one noisy image in a single shot, and proposed a motion-based image merging algorithm to merge the captured images into a high-quality one. Son and Park [22] proposed a patches-based point spread function (PSF) estimation approach by extracting the structure information from the noisy image, along with a channel-dependent deblurring method to obtain the blur-free image. Son et al . [21] proposed a scheme to alternatively estimate the PSF and perform the deconvolution operation on the blurry image using the noisy image as a guiding signal. Gu et al . [7] proposed a method based on Gaussian mixture model to estimate the underlying intensity distribution of the noisy and blurry pairs first and then perform the pixel fusing. Recently, deep neural networks have also been adopted to handle this problem. LSD2 [17] and LSFNet [2] proposed to use convolutional neural networks to fuse the images in an end-to-end manner. SelfIR [27] proposed a self-supervised learning strategy to restore the clean and clear image contents. D2HNet [28] adopted a two-phase pipeline to further increase the visual quality. However, the above methods are designed to enhance the quality of a single input image, which would show inferior performance when handling multiple polarized images.

3 Method

3.1 Problem formulation and overall framework

As shown in Fig. 1, our goal is to reconstruct a clean and clear polarized snapshot with preservation of polarization properties (denoted as I = I α 1 , 2 , 3 , 4 ) from a degraded pair of noisy and blurry polarized snapshots (denoted as L = L α 1 , 2 , 3 , 4 and B = B α 1 , 2 , 3 , 4 respectively), where α 1 , 2 , 3 , 4 = 0 ◦ , 45 ◦ , 90 ◦ , 135 ◦ stand for the polarizer angles of the polarized images in the polarized snapshot respectively. Once I becomes available, high-quality DoP p and AoP θ could be calculated using

where S 0 , 1 , 2 are called the Stokes parameters [13] 2 , which can be computed as

Here, we can see S 0 describes the total intensity of the light, which is polarization-unrelated. In the following, we will use S L 0 , 1 , 2 and S B 0 , 1 , 2 to denote the Stokes parameters of L and B respectively.

The overall reconstruction process of our framework can be formulated as maximizing a posteriori of the output I conditioned on the inputs L and B along with the fusing function f parameterized by Ψ :

ℱenc

c

𝐅𝐅

1

,

2

,

3

c

Multiscale color &

structure features

𝐒𝐒0

B

𝐒𝐒0

L

𝐒𝐒0

t

𝐱𝐱

B

𝐲𝐲

B

𝐲𝐲

t

CSCF

×

3

ℱenc

x

ℱenc

y

CAG

𝐒𝐒1

B

𝐒𝐒2

B

𝐱𝐱

t

𝐲𝐲

′

𝐱𝐱

′

CI

CI

CI

𝐅𝐅x

𝐅𝐅y

Coherence volumes

Phase1: Irradiance restoration

Phase2: Polarization reconstruction

CI

×

3

CI

×

3

c

c

𝐒𝐒1

L

𝐒𝐒2

L

c

𝐒𝐒0

t

𝐈𝐈2

′

𝐈𝐈4

′

𝐈𝐈1

′

𝐈𝐈3

′

ℱmer

𝐅𝐅mer

ℬ

Phase3: Artifact suppression

I

1

I

3

I

2

I

4

Merged dual-branch

features

c

Add & Compute

Color & structure

encoder

CSCF

CSCF: color and structure

cue fusion

x

&

y

encoder

Feature down/up-

sampling & Convs

Dense blocks

Feature merging

U-net backbone

CAG

CAG: coherence-aware

aggregation

CI: coherence injection

CI

CI

CI

ℱenc

s

𝐅𝐅

1

,

2

,

3

s

𝐒𝐒1

L

𝐒𝐒2

L

CSCF

CSCF

CSCF

To solve this maximum a posteriori estimation problem, we design a neural network-based threephase fusing scheme to implement the fusing function f , as shown in Fig. 2. First, the irradiance restoration phase restore the polarization-unrelated high-level irradiance information of the scene, by enhancing S L 0 to obtain the coarse value of the total intensity of the light S t 0 under the guidance of the color and structure cues provided by S B 0 and S B 1 , 2 respectively. Then, we compute ( x t , y t ) , which are the coarse values of the DoP and AoP in a Cartesian coordinate representation, and feed them into the polarization reconstruction phase to obtain the corresponding enhanced values ( x ′ , y ′ ) with the help of ( x B , y B ) (the DoP and AoP of B in a Cartesian coordinate representation) along with the irradiance clues encoded in S t 0 , aiming to establish the physical correlation between the polarized images. Finally, we compute the coarse values of the polarized images I ′ α 1 , 2 , 3 , 4 , and adopt an artifact suppression phase to obtain I α 1 , 2 , 3 , 4 that make up the clean and clear polarized snapshot I , by suppressing the artifacts in the image domain for increasing the quality of details.

3.2 Phase1: Irradiance restoration

This phase aims to restore the polarization-unrelated high-level irradiance information for providing further guidance. As shown in Fig. 1, since L would retain better contours than B , we propose to learn the residual between S L 0 and S t 0 instead of the residual between S B 0 and S t 0 . However, S L 0 usually suffers from color bias and noise, which would increase the difficulty of feature extraction, resulting in erroneous global tone and less salient local structure. Fortunately, despite that S B 0 would suffer from motion blur, it still contains undamaged color information due to the relatively high SNR of B ; besides, S L 1 , 2 could provide distinctive structure information since both of them describe the difference between two polarized images (see Eq. (2)), which would highlight the regions with high gradients. Therefore, we propose to effectively explore the usage of S B 0 and S L 1 , 2 .

Specifically, we first explicitly adopt two modal-specific feature encoders F c enc and F s enc to extract the multiscale color and structure features F c 1 , 2 , 3 and F s 1 , 2 , 3 from S B 0 and S B 1 , 2 respectively for guidance. Then, we propose to use three color and structure cue fusion (CSCF) modules to apply the guidance provided by F c 1 , 2 , 3 and F s 1 , 2 , 3 to F in 1 , 2 , 3 in a successive manner, and output F out 1 , 2 , 3 for restoring S t 0 . Here, F in 1 , 2 , 3 and F out 1 , 2 , 3 denote the multiscale input and output features of the CSCF modules respectively, which are extracted from and fed into the encoder and decoder part of a modified autoencoder architecture [9] where a dense block [11] is inserted into the coarsest layer.

𝐅𝐅

𝑖𝑖

c

Deformable Conv

Bottleneck

Sigmoid

Conv

Conv

𝐅𝐅

𝑖𝑖

in

𝐅𝐅

𝑖𝑖

s

Bottleneck

Conv

Conv

Offset

Modulator

𝐦𝐦𝑖𝑖

𝐛𝐛

𝑖𝑖

∆𝐏𝐏𝑖𝑖

∆𝐌𝐌𝑖𝑖

𝐅𝐅

𝑖𝑖

out

(N,C,H,W)

𝑖𝑖

-th scale

color

feature

𝑖𝑖

-th scale

structure

feature

𝑖𝑖

-th scale

input

feature

𝑖𝑖

-th scale

output

feature

𝐒𝐒0

t

𝐲𝐲

B

𝐲𝐲

t

𝐱𝐱

t

𝐱𝐱

B

Bottleneck

Bottleneck

Bottleneck

C

Conv

Bottleneck

C

Conv

Sigmoid

MLP

Sigmoid

MLP

𝐅𝐅x

𝐅𝐅y

(N,C,H,W)

(N,C,1,1)

(N,C,1,1)

(a)

CSCF

(b)

CAG

𝐀𝐀x

𝐀𝐀y

𝐙𝐙x

𝐙𝐙y

Coherence volumes

CAT

Multiply

Add

Multiply

CAT

CSCF: color and structure cue fusion. The CSCF module aims to address the issues of erroneous global tone and less salient local structure in the feature space. Without losing generality, we describe how the i -th scale ( i = 1 , 2 , 3 ) CSCF takes F c i , F s i , and F in i as the input and output F out i , as shown in Fig. 3 (a). We first learn a multiplier m i and a bias b i from F c i by

where B c denotes a bottleneck block [8] used for feature projection, C m and C b denote two different convolution layers. Then, we apply an affine transformation to F in i using m i and b i , to adjust the color in the feature space for solving the issue of erroneous global tone by

where F t i denote the transformed feature, ⊙ denotes element-wise product operation. Finally, to solve the issue of less salient local structure, we apply a deformable convolution layer [34] D to align the gradients and overcome the possible shifts caused by the exposure interval in the feature space by

where ∆ P i and ∆ M i are the offsets of sampling points and the modulation scalars learned by

where B s , C P , and C M denotes another bottleneck block [8] and convolution layers respectively.

3.3 Phase2: Polarization reconstruction

This phase aims to establish the physical correlation between the polarized images by reconstructing the high-quality DoP and AoP. To achieve it, previous methods usually choose to repair the degenerated values in the image domain [10, 25, 15, 33] or Stokes domain [32] for an indirect reconstruction, since the degeneration patterns of the DoP and AoP could be more complicated than the polarized images or Stokes parameters due to their non-linearity (see Eq. (1)), which could increase the ill-posedness. In contrast, we propose to reconstruct the DoP and AoP in a Cartesian coordinate representation, which can not only relieve the ill-posedness since the non-linearity reduces, but also optimize the values of the DoP and AoP in a direct manner to prevent error accumulation.

Here, we explain what is the Cartesian coordinate representation of the DoP and AoP: as shown in Fig. 4, if we regard p and 2 θ in Eq. (1) as the magnitude and angle of a vector ⃗ S lying inside a unit circle, the Cartesian coordinate representation of ⃗ S could be written as ( x , y ) , which satisfying

𝐱𝐱

𝐲𝐲

𝐩𝐩

2

𝛉𝛉

⃗

𝐒𝐒

⃗

𝐒𝐒

=

𝐩𝐩

<

⃗

𝐒𝐒

= 2

𝛉𝛉

-1

-1

1

1

Unit

circle

Specifically, we propose to learn the residual between ( x t , y t ) and ( x ′ , y ′ ) , with the help of ( x B , y B ) and S t 0 , where

First, we propose to use a coherence-aware aggregation (CAG) module to estimate the coherence volumes F x and F y from x B,t , y B,t , and S t 0 . Then, we adopt two branches for reconstructing x ′ and y ′ respectively. The first branch contains a feature encoder F x enc and three cascaded coherence injection (CI) modules using F x for guidance. Similarly, the second branch contains a feature encoder F y enc and three cascaded CI modules using F y for guidance.

CAG: coherence-aware aggregation. The CAG module aims to aggregate the priors about the coherence between the polarization properties and the irradiance information. As shown in Fig. 3 (b), it contains two symmetrical parts for estimating F x and F y respectively. Here, we only describe how to estimate F x since the estimation process of F y could be similar. We first adopt two bottleneck blocks [8] to extract polarization features and irradiance features from x B,t and S t 0 respectively. Then, we project the extracted features into the coherence features Z x ∈ R N × C × H × W using a convolution layer, where N,C,H,W represent the batch size, number of channels, height, and width respectively. After that, inspired by CBAM [24] that can make full use of the inter-channel relationship of features, we propose to learn an attention vector A x ∈ R N × C × 1 × 1 to recalibrate Z x for obtaining F x ∈ R N × C × H × W :

where P denotes the global average pooling operation and MLP denotes a multi-layer perceptron.

CI: coherence injection. The CI module aims to inject the priors about coherence into the reconstruction of x t and y t . To enlarge the receptive field and include long-range association, we choose to use Transformer modules [6] with cross-attention layers. Taking one of the CI module in the first branch (the branch for reconstructing x t ) as an example, denoting its input as F in x (from the previous CI module or F x enc ) and F x (from the CAG module), we let F in x to serve as the query vector and adopt convolution layers to learn the key vector and the value vector from both F in x and F x.

3.4 Phase3: Artifact suppression

With S t 0 and ( x ′ , y ′ ) available, we could compute the coarse values of the polarized images I ′ α 1 , 2 , 3 , 4 . However, we should not output I ′ α 1 , 2 , 3 , 4 directly since their quality is still not satisfying. This is because S t 0 and ( x ′ , y ′ ) are estimated from two different phases so that the irradiance-polarization consistency would break, bringing artifacts to the details. Therefore, we add an extra phase to refine I ′ α 1 , 2 , 3 , 4 for increasing the quality of details by suppressing the artifacts in the image domain.

Specifically, we propose to learn the residual between I ′ α 1 , 2 , 3 , 4 and I α 1 , 2 , 3 , 4 . According to Eq. (2), we choose to divide I ′ α 1 , 2 , 3 , 4 into two groups ( I ′ α 1 , 3 and I ′ α 2 , 4 ) first to ensure both of them contain the full irradiance information and each of them contains half of the polarization properties. Then, we use two convolution layers to extract the features of I ′ α 1 , 3 and I ′ α 2 , 4 respectively, and adopt a feature merger F mer to obtain the merged dual-group features F mer. After that, a U-Net backbone [18] is used to perform pixel-wise multi-scale feature refinement on F mer, and another two convolution layers are adopted for decoding the refined dual-group features into the residuals of each group.

4 Implementation details

Loss function. The total loss function can be written as

which consists of three terms to optimize the three phases respectively: irradiance loss L s, polarization loss L p, and refinement loss L r. The irradiance loss could be written as

where λ a,b s are set to be 10.0 and 0.05 respectively, L 1 and L perc denote the ℓ 1 loss and perceptual loss respectively, the subscript gt labels the ground truth throughout this paper. The perceptual loss

L perc is defined as

where L 2 denotes the ℓ 2 loss, ϕ h denotes the feature map from h -th layer of VGG-19 network [20] pretrained on ImageNet [19], and we use activations from V GG 3 , 3 convolution layer here. The polarization loss could be written as

where λ a,b,c p are set to be 1.0, 0.15, and 1.0 respectively, L tv denotes the total variation loss, L 1 pol is a polarization-based regularization term to ensure the ratio between x ′ and y ′ defined as

The refinement loss could be written as

where λ a,b r are set to be 10.0 and 100.0 respectively, L 2 pol denotes another polarization-based regularization term defined as

which is similar to the one used in [30] for enforcing the preservation of polarization properties.

Dataset preparation. We propose to generate a synthetic dataset due to the fact that there is no public dataset for our settings. First, we choose the PLIE dataset [32] as our data source. It provides short-exposure polarized snapshots that suffer from low-light noise along with the corresponding high-quality reference snapshots captured by a Lucid Vision Phoenix polarization camera, which could serve as L and I . Then, we adopt the approach proposed in [33] to generate the blurry polarized snapshots that suffer from motion blur, which could be served as B . To generate more severe motion blur for increasing the diversity, we add impulsive variation [1] to the motion trajectories. The images are resized and randomly cropped to 256 × 256 ( 512 × 512 ) pixels in the training (test) set. The training (test) set contains 7500 (300) different images in total.

Training strategy. Our framework is implemented using PyTorch with 2 NVIDIA 2080Ti GPUs, and a two-stage training strategy is applied. First, to ensure a stable initialization of the training process, we train the irradiance restoration phase and the polarization reconstruction phase independently for 300 epochs with learning rates of 0.01 and 0.0001 respectively. Then, we train the entire network for 100 epochs with learning rate of 0.0001, and in this training stage we multiply the loss terms L s,p,r with 5.0, 10.0, and 10.0 respectively. For optimization, we use Adam optimizer [12] with β 1 = 0 . 5 , β 2 = 0 . 999 .

Noisy input

Reference

Ours

PLIE

PolDeblur

p

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

Noisy input

Reference

Ours

PLIE

PolDeblur

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

Noisy input

Reference

Ours

PLIE

PolDeblur

So

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

5 Experiments

5.1 Evaluation on synthetic data

First, we compare our framework with the state-of-the-art polarized image low-light enhancement method PLIE [32] and the only existing polarized image deblurring method PolDeblur [33]. Besides, we also compared with PLIE+ and PolDeblur+ (the improved versions of PLIE [32] and PolDeblur [33], where slight modifications are made to allow them to accept noisy and blurry pairs as the input) to ensure a fair comparison. In addition, four learning-based image enhancement methods designed for conventional images that also fuse noisy and blurry pairs (LSD2 [17], LSFNet [2], SelfIR [27], and D2HNet [28]) are also compared to ensure a comprehensive evaluation. Note that all compared methods are retrained on our dataset. As other methods designed for polarized images [10, 25, 15, 32, 33] do, we not only evaluate the quality of p and θ , but also the quality of S 0 .

To evaluate the results quantitatively, we adopt two frequently-used metrics including PSNR and SSIM. Results are shown in Tab. 1, where our framework consistently outperforms the compared methods on all metrics. These results could demonstrate three points:

Complementarily fusing can improve the quality of polarimetric imaging , which could be deduced from the fact that PLIE+ and PolDeblur+ (which can accept a degraded pair of noisy and blurry polarized snapshots as the input) achieve better performance compared with PLIE [32] and PolDeblur [33] (which can only accept a single degraded polarized snapshot as the input) respectively.

Our framework can utilize the complementary knowledge in a more effective manner , since our framework still outperforms PLIE+ and PolDeblur+ despite that they share the same kind of input as our framework.

Noisy input

Reference

Ours

PLIE

PolDeblur

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

Noisy input

Reference

Ours

PLIE

PolDeblur

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

𝐩𝐩

𝜽𝜽

Reference

Ours

PLIE

PolDeblur

PLIE+

PolDeblur+

LSFNet

SelfIR

D2HNet

𝐒𝐒0

Noisy input

4112

Designing a fusing framework tailored to the polarized images is necessary , since the performance of both LSD2 [17], LSFNet [2], SelfIR [27], and D2HNet [28] is inferior in the task of polarimetric imaging.

Visual quality comparisons are shown in Fig. 5 3 . As for p and θ , our framework can produce clean and clear edges, since it can make full use of the complementary knowledge in a polarization-aware manner, while the compared methods suffer from noisy or blurry artifacts. As for S 0 , our results resemble the reference more closely with less color and structure distortion.

5.2 Evaluation on real data and downstream application

To evaluate on real data, we capture several pairs of noisy and blurry polarized snapshots from various scenes using a Lucid Vision Phoenix polarization camera. Qualitative results are shown in Fig. 6 4 , from which we can see our framework can produce high-quality details.

Besides, in Fig. 7 we show that complementarily fusing can be beneficial to downstream polarizationbased vision application such as reflection removal. Here, we feed a short-exposure noisy, a longexposure blurry, and the fused polarized snapshots into a reflection removal network (RSP [16]) respectively. From the results we can see the reflection-removed image with the fusing process of our framework contains more detailed textures and less reflection contamination.

5.3 Ablation study

To verify the validity of each design choice, we conduct a series of ablation studies and show comparisons in Tab. 2. First, we show the effectiveness of the first phase by substituting it with

Fused by our framework

Noisy

Fused by our framework

Noisy

Reflection-contaminated input

Reflection-removed output

a learning-based image enhancement methods designed for conventional images (LSD2 [17] as Phase1). The reason why we choose LSD2 [17] is that it outperforms other similar methods (LSFNet [2], SelfIR [27], and D2HNet [28]) in restoring S 0 (see Tab. 1). We can see that the performance becomes inferior since our first phase can make full use of the color and structure cues encoded in the Stokes parameters while LSD2 [17] cannot. Then, we show the effectiveness of the proposed CSCF modules in the first phase (W/o CSCF) by substituting them with vanilla convolution layers. We can see that the performance degenerates since the CSCF modules are more suitable for addressing the issues of erroneous global tone and less salient local structure in the feature space. Similarly, we also show the effectiveness of the proposed CAG module and CI modules in the second phase (W/o CAG and W/o CI). We can see that the CAG module can facilitate the establishment of the physical correlation between the polarized images, and the CI modules can offer better optimization to the values of the DoP and AoP. Besides, we demonstrate the advantage of reconstructing the DoP and AoP in a Cartesian coordinate representation (W/o Cartesian) by directly reconstructing their values. We can see that the performance degraded severely due to the non-linearity. Finally, we verify the necessity of the third phase used for refinement (W/o refinement) by removing it. These results show our complete framework achieves the first performance with the proposed specific designs.

6 Conclusion

We propose a quality-improved and property-preserved polarimetric imaging framework by complementarily fusing a degraded pair of noisy and blurry polarized snapshots. By adopting a neural network-based three-phase fusing scheme consisting of irradiance restoration, polarization reconstruction, and artifact suppression, with specially-designed modules tailored to each phase, our framework can produce clean and clear polarized snapshots with high-quality DoP and AoP.

Limitations. Since our framework is designed for reconstructing a single high-quality polarized snapshot from a degraded pair of noisy and blurry polarized snapshots, it cannot reconstruct a polarized video. Besides, it cannot be used to fuse conventional RGB images since our first phase requires the Stokes parameters as part of the input, which are not available in such a setting.

Acknowledgments and Disclosure of Funding

This work was supported in part by National Natural Science Foundation of China under Grant No. 62136001, 62088102, 62276007 and JST-Mirai Program Grant Number JPMJM123G1.

References

Giacomo Boracchi and Alessandro Foi. Modeling the performance of image restoration from motion blur. IEEE Transactions on Image Processing , 21(8):3502-3517, 2012.

Meng Chang, Huajun Feng, Zhihai Xu, and Qi Li. Low-light image restoration with short-and longexposure raw pairs. IEEE Transactions on Multimedia , 24:702-714, 2021.

Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proc. of Computer Vision and Pattern Recognition , 2018.

Byeong-Doo Choi, Seung-Won Jung, and Sung-Jea Ko. Motion-blur-free camera system splitting exposure time. IEEE Transactions on Consumer Electronics , 54(3):981-986, 2008.

Valentin Deschaintre, Yiming Lin, and Abhijeet Ghosh. Deep polarization imaging for 3D shape and SVBRDF acquisition. In Proc. of Computer Vision and Pattern Recognition , 2021.

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 , 2020.

Chunzhi Gu, Xuequan Lu, Ying He, and Chao Zhang. Blur removal via blurred-noisy image pair. IEEE Transactions on Image Processing , 30:345-359, 2020.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proc. of Computer Vision and Pattern Recognition , 2016.

Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science , 313(5786):504-507, 2006.

Haofeng Hu, Yang Lin, Xiaobo Li, Pengfei Qi, and Tiegen Liu. IPLNet: A neural network for intensitypolarization imaging in low light. Optics Letters , 45(22):6162-6165, 2020.

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proc. of Computer Vision and Pattern Recognition , 2017.

Diederik P Kingma and Jimmy Ba. ADAM: A method for stochastic optimization, 2014.

GP Können. Polarized light in nature . CUP Archive, 1985.

Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiˇ rí Matas. DeblurGAN: Blind motion deblurring using conditional adversarial networks. In Proc. of Computer Vision and Pattern Recognition , pages 8183-8192, 2018.

Zhuoxiao Li, Haiyang Jiang, Mingdeng Cao, and Yinqiang Zheng. Polarized color image denoising. In Proc. of Computer Vision and Pattern Recognition , pages 9873-9882, 2023.

Youwei Lyu, Zhaopeng Cui, Si Li, Marc Pollefeys, and Boxin Shi. Reflection separation using a pair of unpolarized and polarized images. In Proc. of Advances in Neural Information Processing Systems , 2019.

Janne Mustaniemi, Juho Kannala, Jiri Matas, Simo Särkkä, and Janne Heikkilä. LSD2 - joint denoising and deblurring of short and long exposure images with CNNs. In Proc. of British Machine Vision , 2020.

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proc. of International Conference on Medical Image Computing and Computer Assisted Intervention , pages 234-241, 2015.

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C Berg, and Li Fei-Fei. ImageNet large scale visual recognition challenge. International Journal of Computer Vision , 115(3):211-252, 2015.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014.

Chang-Hwan Son, Hyunseung Choo, and Hyung-Min Park. Image-pair-based deblurring with spatially varying norms and noisy image updating. Journal of Visual Communication and Image Representation , 24(8):1303-1315, 2013.

Chang-Hwan Son and Hyung-Min Park. A pair of noisy/blurry patches-based PSF estimation and channeldependent deblurring. IEEE Transactions on Consumer Electronics , 57(4):1791-1799, 2011.

Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, and Jiaya Jia. Scale-recurrent network for deep image deblurring. In Proc. of Computer Vision and Pattern Recognition , pages 8174-8182, 2018.

Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. CBAM: Convolutional block attention module. In Proc. of European Conference on Computer Vision , pages 3-19, 2018.

Xiuyu Xu, Minjie Wan, Junyu Ge, Hao Chen, Xingcheng Zhu, Xiaojie Zhang, Qian Chen, and Guohua Gu. ColorPolarNet: Residual dense network-based chromatic intensity-polarization imaging in low-light environment. IEEE Transactions on Instrumentation and Measurement , 71:1-10, 2022.

Lu Yuan, Jian Sun, Long Quan, and Heung-Yeung Shum. Image deblurring with blurred/noisy image pairs. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH) , 26(3):1-es, 2007.

Zhilu Zhang, RongJian Xu, Ming Liu, Zifei Yan, and Wangmeng Zuo. Self-supervised image restoration with blurry and noisy pairs. Advances in Neural Information Processing Systems , 35:29179-29191, 2022.

Yuzhi Zhao, Yongzhe Xu, Qiong Yan, Dingdong Yang, Xuehui Wang, and Lai-Man Po. D2HNet: Joint denoising and deblurring with hierarchical network for robust night image restoration. In Proc. of European Conference on Computer Vision , pages 91-110, 2022.

Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In Proc. of International Conference on Computer Vision , 2021.

Chu Zhou, Yufei Han, Minggui Teng, Jin Han, Si Li, Chao Xu, and Boxin Shi. Polarization guided HDR reconstruction via pixel-wise depolarization. IEEE Transactions on Image Processing , 32:1774-1787, 2023.

Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, and Boxin Shi. Learning to dehaze with polarization. In Proc. of Advances in Neural Information Processing Systems , 2021.

Chu Zhou, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, and Boxin Shi. Polarization-aware low-light image enhancement. In Proc. of the AAAI Conference on Artificial Intelligence , pages 3742-3750, 2023.

Chu Zhou, Minggui Teng, Xinyu Zhou, Chao Xu, and Boxin Sh. Learning to deblur polarized images. arXiv preprint arXiv:2402.18134 , 2024.

Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable ConvNets v2: More deformable, better results. In Proc. of Computer Vision and Pattern Recognition , pages 9308-9316, 2019.

A Appendix

Please see the supplemental material for the things mentioned in Footnote 2, 3, and 4.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/public/ guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https://nips.cc/public/ guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: There is no societal impact of the work performed.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: The paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: No need for justification since the answer is 'Yes'.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: The paper does not release new assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method to fuse a pair of short-exposure (noisy) and long-exposure (blurry) captures to produce clean and clear polarized snapshots. The proposed method consists of three phases to reconstruct the irradiance, texture, and polarization.\n\nSTRENGTHS:\nThe paper is well-written, the proposed method is well-described, and experiments using both synthetic and real-world data are conducted to evaluate the effectiveness of the proposed method.\n\nWEAKNESSES:\nIt is difficult to fully understand and evaluate the real-world experiments. Please refer to the corresponding questions for further details.\n\nQUESTIONS:\n1. When generating the synthetic dataset, what are the short and long exposure times? Have you considered different exposure time settings and varying motion blur intensities? Have you added sensor noise to the synthetic images?\n\n2. How is the reference image obtained when conducting real-world experiments (Fig. 6)? There are few details about the real-world experiments in the paper.\n\n3. In Fig. 7, is it possible to add comparison results from other methods? It is difficult to evaluate the final results solely based on the proposed framework. Also, how should one understand the difference between the “reflection-contaminated input” and the “reflection-removed output”? Again, there are few details about the real-world experiments.\n\n4. In line 56, it is not accurate to claim that this is “the first time applying a fusing strategy to polarimetric imaging.” There are existing pieces of literature on polarimetric image fusion, such as “Semantic-guided polarization image fusion method based on a dual-discriminator GAN” by Liu et al., and “Polarization-driven semantic segmentation via efficient attention-bridged fusion” by Xiang et al.\n\nLIMITATIONS:\nLimitations of this paper are well discussed.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a polarimetric imaging framework that can produce clean and clear polarized snapshots by complementarily fusing a degraded pair of noisy and blurry ones. It adopts a neural network-based three-phase fusing scheme with specially designed modules tailored to each phase, which can not only improve the image quality but also preserve the polarization properties.\n\nSTRENGTHS:\nThe idea of using complementarily fusing to achieve quality-improved and property-preserved polarimetric imaging is novel and reasonable. As obtaining high-quality polarized images is significant in polarization-based vision applications, while previous methods based on single modality (either noisy or blurry) tend to suffer from various artifacts, the proposed method could be a practical way to increase the performance of polarization-based vision applications.\n\nThe network module designs are also reasonable. All modules (Irradiance restoration, Polarization reconstruction, and Artifact suppression) are carefully and specially designed to solve the problems in the fusing process, which means the authors do spend efforts in observing and analyzing the properties in both the noisy and blurry polarized snapshots. \n\nThe idea is clearly presented, and the experiments are sufficient. The performance improvement shows that the proposed method is effective.\n\nIn addition to the experiments on synthetic and real data, the authors also show the results of reflection removal, which makes the paper convincing.\n\nWEAKNESSES:\nThe authors say that they adopt the PLIE dataset [32] as the source data to generate their own dataset. However, they do not tell the reasons why to choose the PILE dataset [32]. For example, [25] also provides a dataset (LLCP dataset) similar to the PLIE dataset [32], so why not choose the LLCP dataset as the source data? Any reasons?\n\nQUESTIONS:\nIt seems that using polarizers instead of polarization cameras can also capture the polarized images. However, in some cases one can only use a polarizer to capture polarized images instead of a polarization camera. Can the proposed method be used to process the data captured using polarizers?\n\nLIMITATIONS:\nThe authors have adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-08"}

comments: SUMMARY:
This paper proposes the first method for polarimetric image enhancement by fusing noisy and blurry pairs. While a short exposure polarimetric image produces sharp but noisy DoP and AoP, a long exposure makes them smooth but blurred. To effectively exploit the complementary advantages of these two images and satisfy the physics constraints of the polarimetric image, this paper proposes a three-phase fusing scheme. Experimental results show that the proposed method outperforms existing polarimetric image enhancement methods.

STRENGTHS:
+ The first method for polarimetric image enhancement by fusing noisy and blurry pairs.
+ Propose a novel fusing scheme to effectively use complementary polarimetric information of noisy and blurry pairs and retain polarimetric cues by directly processing DoP and AoP.
+ Experimentally validate the effectiveness of the fusion of noisy and blurry polarimetric image pairs and the proposed network. The accurate restoration of polarimetric cues is critical for downstream tasks.

WEAKNESSES:
- While the proposed method improves the pSNR of DoP and AoP, their SSIMs are almost the same as PLIE [32].
- Requiring two shots is undesirable for some downstream tasks.

QUESTIONS:
- Why does not the proposed method significantly improve the SSIMs of DoP and AoP? How does the improvement of PSNR affect the downstream tasks?
- How are reference images of real data obtained? Can they be used for quantitative evaluation?
- The visual comparisons of the ablation study will help understand the effectiveness of each component.

LIMITATIONS:
Yes.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-12