Reveal Object in Lensless Photography via Region Gaze and Amplification
Authors: Yin Xiangjun
Huihui Yue
Venue: ICLR 2025 Poster (2025)
Keywords: Lensless Imaging; Computational Imaging; Region Gaze; Region Amplifier;  Concealed Object Detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Detecting concealed objects, such as in vivo lesions or camouflage, requires customized imaging systems. Lensless cameras, being compact and flexible, offer a promising alternative to bulky lens systems. However, the absence of lenses leads to measurements lacking visual semantics, posing significant challenges for concealed object detection (COD). To tackle this issue, we propose a region gaze-amplification network (RGANet) for progressively exploiting concealed objects from lensless imaging measurements. Specifically, a region gaze module (RGM) is proposed to mine spatial-frequency cues informed by biological and psychological mechanisms, and a region amplifier (RA) is designed to amplify the details of object regions to enhance COD performance. Furthermore, we contribute the first relevant dataset as a benchmark to prosper the lensless imaging community. Extensive experiments demonstrate the exciting performance of our method. Our codes will be released in \url{https://github.com/YXJ-NTU/Lensless-COD}.

Published as a conference paper at ICLR 2025
REVEAL OBJECT IN LENSLESS PHOTOGRAPHY VIA REGION GAZE AND AMPLIFICATION
Xiangjun Yin' Huihui Yue?*
Centre for Integrated Circuits and Systems (CICS), Nanyang Technological University, Singapore Schooo of Physical and Mathematical Sciences, Nanyang Technological University, Singapore xi xiangjun. yin@ntu.. edu. sg, huihui, queentu.edu.c0
ABSTRACT
Detecting concealed objects, such as in vivo lesions camouflage, requires cus- tomized imaging systems. Lensless cameras, being compact and flexible, offer promising alternative to bulky lens systems. However, the absence of lenses leads to measurements lacking visual semantics, posing significant challenges for concealed object detection (COD). To tackle this issue, we propose a region gaze-amplification network (RGANet) for progressively exploiting concealed ob- jects from lensless imaging measurements. Specifically, region gaze module (RGM) is proposed to mine spatial- frequencyf cues informed by biological and psychological mechanisms, and region amplifier (RA) is designed amplify the details of object regions to enhance COD performance. Furthermore, we con- tribute the first relevant dataset as benchmark to prosper the lensless imaging community. Extensive experiments demonstrate the exciting performance of our method. Our codes will be released at tottps://githin com/ YXU-NTU/
Lensless-Con
INTRODUCTION
Concealed object detection (COD) et (2023) Sun et al. (2024) is an emerging task that plays an essential role in many visual applications, such as medical image analysis (Luo al. (2022). ). as it aims to extract objects hidden in the scene. Various models (Fan et al. (2022); Mei et al. (2021) have been developed and performed well on relevant datasets collected by current imaging systems. However, owing to their bulky size, existing imaging systems cannot access the tight areas. Accordingly, developing miniature and compact imaging systems has become imperative to overcome these challenges. Due to replacing lenses with optical masks (Khan et al. (2022); [Asif al. Boominahhan et al. (2020); Antipa et al. (2018) and calculations, lensless cameras are allowed to be flexibly miniaturized (Tan et al. (2019)) Thus, the aggregation of lensless cameras into COD is potential option.
As shown in Fig. (a), unlike existing imaging systems that enable scene-resembling imaging, op- tical mask-based lensless camera modulates the scene radiances into encoded patterns (i.e., lensless imaging measurements) without any visual information, which leads to severe challenges in COD for lensless cameras. The primary challenges are: 1) Lensless imaging lacks traditional visual fea- tures, making it challenging extract task-relevant information from the data; 2) The complexity of the data impose greater demands on model training and optimization, particularly in noise suppres- sion and key information retention; and The inherent difficulties of the COD task itself. Existing lensless imaging studies present several closely related topics that offer valuable insights into COD within the context of lensless imaging- recent study (Yin et al. (2022)) proposes spatial feature learning (SFL) module for obtaining partial coarse spatial information to facilitate the detection of objects lensless imaging while maintaining low computational costs. Inspired by this, we design an optical-aware feature extraction (OFE) module for learning spatial features. Note that unlike the supervised SFL module (Yin et al. (2022)), our OFE module is not supervised but learns in concert with the subsequent task, thus learning more meaningful features.
Recent studies (Rao et al. (002)); Lin et al. (2023) have shown that according to biological and psy- chological investigations, Trequency information helps to effectively mitigate semantic clutter and
Corresponning author: hui wwwiiiyue@nnteeeee

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1. COD based lens-based camera
||I
Capturn
Leus- based
Segmentation Network
Prediction
II. Our COD based lemsless camera
Capture
Scene
Lensless
Masurement
Segmentation Network
Prediction
Massurment
Real Scree
Ground Truth
Ours
ZoomNet
Figure 1: Illustrations of systems and visual examples COD for lensless imaging measurements: Comparison between our COD based on lensless camera and the current COD based on traditional camera; The results by state-of-the-art COD method (ZoomNet Pang et al. (2022)) and our RGANet. Our method can mine concealed objects from the lensless imaging measurements.
improve COD performance. Therefore, to boost the detection of concealed objects against back- grounds, frequency cues are one the key elements. Inspired by this, we propose region gaze encoding (FCE) component and spatial information coding (SIE) component are designed to extract frequency cues and spatial cues respectively, and then spatial-frequency feature fusion (SFFF) component built for collaborative learning of spa- tial cues and frequency cues. Further, another study (Xing et al. (2023)) observed that maintaining proximity to an object plays crucial role in enhancing the perception of intricate details. This de- tail perception at high-level, in turn, contributes significantly to the overall process of recognizing and identifying the object. To this end, we design region amplifier (RA) simulate this proxim- ity mechanism to further enhance the COD performance by locally amplifying the region details. Based on the above description, we propose region gaze-amplification network (RGANet), which consists of OFE module, two RGMs, and RA, well as hierarchical feature decoding (HFD) module for fine reasoning. Some visualization examples Fig. demonstrate that our RGANet can effectively detect concealed objects from lensless imaging measurements. In nutshell, our main contributions are summarized as:
module (RGM), where frequency
To the best of our knowledge, are the first to investigate the detection of concealed objects lensless imaging, demonstrating the potential of lensless imaging for various high-level tasks.
We propose region gaze-amplification network (RGANet) for COD in lensless imaging. In the RGANet, the region gaze modules (RGMs) based on the apatial-frequene collabo- rative learning strategy proposed to recognize the object regions, and region amplifier (RA) based on local attention is designed to amplify the region details.
We contribute corresponding datasets as benchmarks and extensive experiments demon- strate that our method can accurately detect concealed objects from lensless imaging mea- surements.
RELATED WORK
2.1 CONCEALED OBJECT DETECTION (COD)
COD is challenging due to the high similarity between objects and their surrounding environments. Previous research has been devoted to this challenge using variety of strategies. Le et al. (Trung-
Nghia et al. (2019)) proposed to aggregate classifieds into pixel-level detection. Fan et al. (Fan et al.
(222 developed the SINet to advance the field of COD. Chen el al. (Chen al. 2022 designed
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to integrate cross-level features of concealed objects. Liu et al.(Liu et al. (2023) built MSCAF- Net to focus on multi-scale context-aware cues. Mei et al. (Mei et al. (202T) proposed to first localize potential objects and then focus discovering and eliminating interferences for progressive reasoning. Liu et al. (Liu et al. (2022) constructed an online confidence estimattion network to model the aleatoric uncertainty for COD. Pang et al. (Pang al. (2022) used mixed-scale triple network to focus on the objects by mimicking the zoom-in and zoom-out behavior of humans when viewing blurry images. Ma et al. (Ma & Sun (2023) proposed cross-level interaction network based on scale-aware augmentation for the COD task. However, the above studies use clear natural images as input to perform COD and cannot be directly applied lensless imaging. By exploring spatial-frequencc cues for Region Gaze and proposing local amplification mechanisms for detail magnification, we are the first to peform COD for lensless imaging measurements.
2.2 LENSLESS IMAGING
Lensless imaging is increasingly recognized for its potential to overcome size limitations in smart- phone photography and micro-robotics applications. The core of lensless cameras is optical masks designed with various encoding elements, including amplitude masks (Khan et al. (2022); Asif et al. (017 and phase masks (Boominathan al. (2020); |Attiaa et al. (2018). The diverse design of optical masks has given rise to various prototypes, including the Fresnel zone aperture (FZA) cam-
era (Wu et al. (2020) 202T)), FlatCam (Khan et al. (2022); Assif al. (2017)) PHlatCam (Boomi- nathan et al.] (0200) and DiffuserCam (Antipa et al. (2018); Monakhova et al. (2020); Cai et al. (2020). They have demonstrated potential in various applications such as hyperspectral imag-
ing Monakhova et al.) (2020), fluorescence microscopy imaging (Adams et al. (2017)) light field
encoding (Cai et al. (2020), and depth information acquisition (Zheng & Salman Asif (2020). ).
Recently, some studies have begun to explore high-level tasks in lensless imaging, such gender estimation (Pan et al. (2021b), recognition (Pan et al. (2021a)), ). face verification (Tan et al. (0099;;
Cai et al. (2024)) and object segmentation (Yin et al. 2022 2024)). These studies provide proof- of-concept investigation for the potential of inference tasks using lensless imaging measurements.
However, these above efforts have yet to demonstrate performance in performing COD task or ex- plicitly address the grand limits posed by such methods.
2.3 LEARNING IN THE FREQUENCY DOMAIN
Frequency information has been widely employed in convolutional neural networks (CNNs) for im- age enhancement, data compression, object detection (Shao et al. (2023); Li et al. (2022); ; Wang & Sertel (2021); Liu et al. (2024)) ), etc. Previous studies (Wang al. (2022) characterized the edges of objects and smooth regions by high and low-frequency semantics, respectively. Additionally, some researchers (Mi et al. (2022) promoted channel recognition networks into frequency domains. Compressing vision Transformers (Kong et al. (2023)) has also been explored by removing or com- pressing low-frequency components. Moreover, Rao el al. (Rao et al. (2022)) explicitly modeled the semantic information of different frequencies and accurately guided the semantic alignment of objects. In the context of COD, frequency learning has been employed complex
mitigate the influence of texture information (Lin et al. (2023); y. Sun et al. (2024)). However, there still needs to be more exploration regarding the intensive prediction of the interaction between fre- quency and spatial domains. In contrast, our method improves COD performance by facilitating collaborative learning between the frequency and spatial domains, thereby obtaining richer cues.
3
METHODOL.OGY
3.1 OVERVIEW
As depicted in Fig. based on a lensless imaging prototype, i.e., PHlatCam (Boominathan et al. (2020)) we propose the RGANet for COD in lensless imaging. which contains an -aticel-aware feature extraction (OFE) module, two region gaze modules (RGMs), a region amplifier (RA), and a hierarchical feature decoding (HFD) module. Specifically, we employ the OFE module to learn the beneficial underlying semantics for COD. Then, we propose two RGMs to progressively reason concealed objects by mining spatial and frequency cues. Furthermore, the RA embedded between the two RGMs is exploited to magnify the details of object regions to drive COD performance. Finally, the outputs of two RGMs aggregated in the HFD module to obtain refinement results. Our RGANet provides a comprehensive workflow for performing COD on lensless imaging and demonstrates promising results.
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Pipeline
FCE
FCE
SEEP
OFE
SEFF
RA
SEFF
HFD
SIE
RGM
RGM
Prediction
SEFF
e
HFD
FTansen wiw.
Hamamard Kcd
Signal
Tradeemutton Weight
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Spoon Feequegg
1T,
PVT Stape?
TT.
BGM:
R3
Regian Case Viatale
Shamoria Roatur boooding
Figure 2: Overview of our RGANet. It consists of an optical-aware feature extraction (OFE) mod- ule, region gaze modules (RGMs), a region amplifier (RA), and hierarchical feature decoding (HFD) module. Specifically, we employ the OFE module to learn the meaningful underlying se- manties for COD, and the two RGMs progressively reason concealed objects by mining spatial and frequency cues. Furthermore, the RA embedded between the two RGMs is used to magnify the de- tails of regions to auxiliary the detection of concealed objects. Finally, the outputs the two RGMs are fed into the HFD module to refine the final results.
3.2 OPTICAL-AWARE FEATURE EXTRACTION
Among various lensless imaging prototypes, PHlatCam (Boominathan et al. (2020)) is promising for its high luminous flux, lightweight, and low cost. Given this, we perform our work on PHlatCam. The imaging model of PHlatCam can be formulated convolution model:
Y A +
(1)
where * is the convolution operator, A is the point-spread- fun.ctionnonnnnnnnooooo (PSF), which is the pattern pro- jected onto the image sensor by the mask under the illumination of a single-point light source. X is the underlying scenes, Y is the measurements captured by image sensor, and denotes the noise.
Our work focuses on detecting concealed objects in the scene radiance X from the lensless imaging measurements Y. However, Y do not inherently contain visual cues of concealed objects. To drive COD for lensless imaging, we design an OFE module with a Wiener filtering mechanism as
Conj (F(Ag)
lOFE = =
OF()))
(2)
K2 + [F(A)]
where F and F-1 denote the fast Fourier transform (FFT) and the inverse FFT (IFFT) operations, respectively. Conj(.) is the conjugate operation and @ is Hadamard multiplication. In the OFE module, PSF Ag and regularization parameter K₂ learnable. Note that unlike Khan et al. (002 Boominathan et al.] (2020), our OFE module does not act as visual reconstruction to satisfy the requirements the human eye but rather collaborates with the back-end design reason about the semantics that will benefit COD, and effectively addressing the challenges of lensless imaging.
3.3 REGION GAZE VIA SPATIAL- COLLABORATIVE LEARNING
We propose RGM that incorporates frequency and spatial features in collaborative learning manner. In the RGM, a spatial information encoding (SIE) component and frequency cue encoding (FCE) component designed to extract spatial cues and frequency cucs, respectively, and then spatial-frequena feature fusion (SFFF) component is built for collaborative learning of spatial cues and frequency cues. In our method, two RGMs coupled to refine the predictions incrementally. Since both RGMs share the same structure, we only detail the first RGM.
Spatial Information Encoding, Recent study (Wang al. (201)) shows that Transformer-basee architectures, handling global features better than traditional CNNs. Therefore. we use the Pyra- mid Vision Transformer (PVTv2) build the SIE component for extracting global features and
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Patchalizaiion
DCT
F
Output
MLP
MLP
Flatten
F
MLP
J.
Flatten
I
Frequency Selection
F#
Prequency Seuaration
Stale
DCor
Figure 3: Illustrations of the FCE component. It contains the DCT operation, frequency separation, and frequency selection for the collection of frequency cues.
understanding lensless semantic scenes. PVTv2, with its pyramidal structure, efficiently captures multi-scale, high-resolution features through progressive shrinking strategy and spatial reduction attention. To adapt it for the lensless COD task, we remove the classification layer and design a COD head to generate multi-scale feature maps across four stages (Ti, T2, T3, and T4 from bottom to up), as illustrated in Fig. 2
Frequency Cue Encoding. We design the FCE to mine frequency cues, shown in Fig.
First, we divide ToPE E Hx33 into set of non-overlapping 8 x patches and obtain {pe, | sis 8 $ W at color channel c. Considering that the FFT leads to the plural that the network hardly handles, Fi, (DC((F 11 S < 3: < S g each value of the feature Fi, belongs certain frequency band.
DCT to transform each patch into frequency spectrum as
W FE, & R8x8 corresponds to 64 frequency band,
Second, we divide Pi, into two parts, i.e., low-/high-frequeecyy component (F and (F()) by
(Pt)) Fiy (m, Im oml S r, oml Sr ) Fi, (m,n). otherwise
where (Om 0n) denotes the coordinates of the starting point of the image, i.e., (0, 0). We use (m,, n) to index the value of FY, at position (m, n). The |.| is the absolute value. Note that is a learnable parameter to achieve adaptive separation frequencies (Each ( (m, n) from the same patch shares the same to maintain consistency of dimension).
Third, we flatten the spectrums into a vector that aggregates all the components with same frequency into the exact location of each channel by I Flatten ((F!))) II, Flatten ((F)))).
Fourth, we adaptively select high high-/low-free freeuuryyy features I and I2, through some channel-mixing multi-layer perceptrons (MLP) (Tolstikhin et al.) (202T)) consisting of fully-connect (FC) layer, batch normalization (BN), and a ReLU. Specifically, we feed 1 and I into two separate channel- mixing MLPs to filter feature and concatenate their outputs, and then use another channel-mixin MLP (from bottom to up) to adaptively select frequency features Il. The above steps are formulated as I MLP (Cat (MLP (I() .MLP (IE)))) Cat(.) is the concatenation operation, and the MLP (.) is the channel-mixing MLP capturing the correlation among each patch of input features.
Finally, we obtain frequency feature If by a channel-mixing MLP and double-layer convolution as I 1DConv(MLP)) (I()), DConv(.) is two cascaded 3 x 3 convolution layers. To match the scale of outputs from the SIE component, 1f is scaled to the corresponding size, i.c., I 1/. I/ and If. Note that unlike the fixed thresholds used in previous studies Cong et al. (0023); Lin et al. 2023 Sun al. 2024). here introduce adaptive thresholding mechanism to distinguish
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high-frequency information from low-freq www-freuunccy components. This design advantage allows for better differentiation between noise and signal, reducing the impact of noise.
Frequency Feature Fusion. The frequency features are discriminative for concealed ob- jeets, while spatial features have a larger receptive field for compensating. Therefore, we build the SFFF component to fuse the above two features, as shown in Fig. 2
First, we filter the frequency features to extract information by !! Convirxi(()), where Conv17x17(.) is a 17 x 17 convolution layer.
Second, we fuse the 1 and the features from the SIE component, and each 1 2, 3, 4) is concatenated and fed into two cascaded convolutions with 4x channels to output 8 E BBxWxn
3,2,3,4) 4). We reshape S to HIW n for obtaining the fusion ma- trix W1 E RRI HW for space domain, and W2 E #WWw* for frequency domain by W1 S (5²) W2 5 (5t)
Third, we align the feature maps (T;, H/}} by feature correlation operation. Specifically, the trans- formations Wi and W3 multiplied with features (Ti,
(T J and then multiplied with two learn- ing vectors (i.e., Vi E RIxC and Vi E RIxO) respectively to adjust the channel infor- mation. The output I and I are fused by addition operator. The above step is formulated as I2 Wi T. Vi, Ij W2 I! Vj, Ig = I7. + Ijz, @ is the matrix multiplier.
Fourth, we obtain the fused features by I DConv(I:). These fused features are decoded in a bottom-up manner and output the prediction maps as
(4) The SFFF component uses discriminative frequency information search for concealed objects while maintaining the spatial cues to ensure the details of the entities. Note that our network uses two RGMs with outputs as shown in Eq. (4). To distinguish between the two results, we rewrite the output of the first RGM as Pai and the second as Pa2.
Pa DCDDDCCovraaii IT.. I.)).
3.4 REGION AMPLIFIER VIA LOCAL ATTENTION LEARNING
Concealed objects often appear as small, obscured, or resembling the backgrounds, which makes detecting such objects susceptible to misses and errors. Inspired by the fundamental phenomenon that humans tend to magnify the difference between the observed regions and the backgrounds, design the Region Amplifier (RA) to compress the background regions and magnify the concealed object regions. We transform the output of first RGM, i.e., PaI, into attention map M, as M Convex9 (Pa1), and the ConVax9(-) is 9 X 9 convolution layer, exploited to expand the originally predicted region to cover the entire object region. Then, magnify the concealed objects based on the attention map M. As in (Zheng et al. (2019)), the marginal distribution is obtained by the maximization of M over x axis and y axis
11
Mn(n) = Σ
max 1StSW
M..: Mn Σ
max Mr,s 15e5H
(5)
where W and H are the width and height of M, respectively. Given the output of OFE module, i.e., IOFE, the sampling function @ (1OFE, M) is defined as
@ (IOFE, M M),, (JOFE))r;"0),,7166
(6)
where M-) and (.) indicate the inverse operation of Eq. (5) Thus we can obtain the final result of local magnification as JRA = @ (TOFE, M). Note that our RA module aims to amplify region of interests (Rols) for secondary recognition, which significantly enhances the reconstruction quality.
3.5 HIERARCHICAL FEATURE DECODING
The concealed objects are progressively highlighted by magnification and Since the boundaries of concealed objects are often ambiguous, the above processing performance needs to be enhanced. An intuitive method is to introduce attention mechanisms to explore the in- line association of different pixels, which may contribute to determining minor differences between
cues.

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boundaries and backgrounds. As shown in Fig. 2 propose the HFD module, which fuses the output of the two RGMs, i.c., Pai and Paz, and then enhances the object regions with an attention mechanism to output more refined predictions.
3.6 LOss FUNCTION
To well train, we combine the weighted BCE loss Lune and weighted IoU loss L (Wei et al. (2220), that is, L, = LLBCE + wiou to perform supervised learning on the outputs of the two RGMs and the final output. The final loss function is given
AA L. (Pa1. P&t (Paz: Pat) + L, (Pinal, Pgt),
(7)
where Pai, Paz are the outputs of 1st RGM, 2nd RGM, respectively. Panal the final result of HFD module and P&t is the ground truth.
4 EXPERIMENTS
4.1 DATASETS
To perform COD for lensless imaging, we develop the datasets, including the training dataset and testing dataset. The dataset formation process is shown in Fig. 7in Appendix. The specific details are as follows.
Simulated Data Generation. The simulated data is collected from four famous COD datasets, in- cluding CAMO (Trung-Nghh et al. (2019)) CHAMELEON (Przemystaw), CODIOK (Fan et al. (2022) ) and NC4K (Lvv et al.J (2021). We select 1857 of these images, then generate the corre- sponding simulated lensless imaging measurements by the following forward imaging model
(8) where X is the underlying scene, A is the PSF bound to the lensless camera. The N (u",o*) is the noise, which we set as Gaussian distribution with μf and of = 0.1 * max (X*). Then combined with the existing paired label maps, obtain the SLCOD dataset, shown in Fig. 7
=== (F (A7) o F(X)) +N " E {R, G)
Real Data Acquisition. The real-scene data is acquired by PHlatCam from display captured dataset (Khan et al. (2022) containing 1000 categories. First, exclude unsuitable scenes to obtain 2600 paired images. Then we use Fiseg software to annotate and acquire label maps for performing COD for lensless imaging measurements. Finally, we double-check and re-adjust the labeled maps with sigmificant differences to keep the annotation precision, ultimately creating the DLCOD dataset shown in Fig. Note that DLCOD dataset and the dataset in Yin al. (2022) both originate from the same source: a 10k subset of ImageNet. Unlike our dataset, Yin et al. 2022) targets general object segmentation with broader selection criteria, resulting pairs. Thus some overlap is unavoidable, our analysis shows 326 overlapping data pairs.
larger subset of 5.9k
Dataset Splitting. We split the formed dataset into multiple datasets for training and testing. For training, we randomly select 2060 pairs from DLCOD and merge them with SLCOD to generate a training set containing 3917 paired data. For testing. we divide the remaining pairwise data of DLCOD into two datasets, i.c., Test-Easy with 220 paired data and Test-Hard with 320 paired data, according to the difficulty of double-checking. Figure shows some examples our datasets.
4.2 SETUPS
Evaluation Metries. We apply four evaluation metrics for comprehensive comparisons, i.e., mean absolute error (M), mean E-measure (Eş) (Fan et al. (202I)). weighted F-measure(F))) (Margolin et al. 2014)), and S-measure (S..) (Fan et al. (2017)).
Implementation Bodies. In our method, the backbone is initialized with the pre-trained PVT on ImageNet and subsequently trained alongside other components. The ADAM optimizer is used for training with "cosine" learning rate scheduling policy defined as (r 0.5 init x (1 +1++ cos(TT epoch/ /aad///aa..aa htt///aarpppppe _eno_e).ch)). Here, the learning rate Ir is initialized with init__ 5 x 10-4, and the total training epoch is set to max_epoch = 100 with epoch ranging from to max_epock The batch size is configured as 8. All experiments are conducted on Linux 20.04 server with NVIDIA GTX 3090, utilizing PyTorch 1.8.0.
4.3 COMPARISONS WITH THE STATE-OF-TE--AE BASELINES
Compared Baselines. We compare our RGANet against two kinds of baseline methods: (1) lens- less inference-base methods, such as FyeCol) (You et al. (2033) LII T (Pan al. (2021a)) and

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Table 1: Quantitative results of COD for lensless imaging measurements using method and state-of-the-art baselines on Test-Easy and Test-Hard datasets. Best results are highlighted in red.
Test-Easy
Test-Hard
Method
FLOPs (G) # Param (M)
F5
M Fig So
Pg
M Fig Sa f
EyeCoD
84.37
26.92
0.712 0,131 0.819 0.791
0,563
0.162 0.745 0,710
I.IIT
44.35
17.23
0.743 0.110 0.832 0.802
0.527 0.167 0.741 0.651
LOINet
6.42
25.31
0.762 0.103 0.853 0.821
0.624 0.122 0.779 0.733
MSCAF-Net
63.04
30.32
0.697 0.131 0.812 0.788
0.563
0,161 0.790 0.710
OCENet
13.32
55.01
0.623 0.163 0.851 0.769
0,511
0.182 0.811 0.709
ZoomNet
39.41
32.58
0.714 0.126 0.821 0.782
0.619
0.121 0.804 0.717
48.62
39.45
0.815 0.079 0.896 0.834
0.705
0.098
0.845 0.770
640
(h)
Qualitative comparisons between method and state-of-the-arr baselines Test-Easy dataset. The (d)-() denote the results of RGANet (ours), EyeCoD (You et al. (2023). LLITT (Pan
Figure 4:
al. (021aa), LOINet (Yin al. (2022)) ) MSCAF-Net (Liu al. (2023)), OCENet (Liu et al.
(2022) ), and ZoomNet (Pang et al. (2022)). ). The (c) shows label maps corresponding to lensless
imaging measurements (a) and underlying scenes (b).
LOINet Yin et al. ); (2) state-of-the COD methods, including MSCAF-Net (Liu et al. 2023 OCENet (Liu al. (2022). and ZoomNet (Pang al. (2022)). Prediction maps for all the mentioned methods are generated through re-training their models using open-source codes and a consistent OFE module for equitable comparisons. Moreover, the evaluation of all prediction maps is performed using identical code, ensuring fair and standardized evaluation.
Qualitative Evaluation. Figs. and 5 illustrate the qualitative results obtained on the Test-Easy and Herd-Hard datasets, illustrating the effectiveness of our method in accurately performing COD for lensless imaging measurements. As demonstrated in the comparative results, our method outper- forms alternative approaches in inferring a more comprehensive object structure. The success of our approach can be attributed to two key factors: (1) the incorporation of an encoder-decoder frame- work within the two RGMs facilitates firiiial--ruunncyy a local amplification mechanism contributes to the preservation of intricate details and boundarics.
mining, and (2) the implementation of
Quantitative Evaluation. Table displays the quantitative results of our method in comparison to state-of- of-the-art baselines on the Test-Easy and Test-Hard datasets. Our method surpasses all the compared methods across all metries. On the Test-Easy dataset, method achieves significant decrease in M by 23.3% and an improvement in F by 7.0% compared with the excellent method, LOINet. On the Hest-Hard dataset, our method achieves notable decrease in M by 19.7% and an improvement FW by 13.0% compared with LOINet.
Complexity Analysis. We further provide the computational complexity of each method in terms of FLOPs and the number of parameters (#Param) in Table [[] The two metrics bound to our method are an intermediate level among all compared methods, but our method outperforms these
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(c)
(h)
Figure 5: Qualitative comparisons between method and state-of-the-art baselines on Test-Hard
dataset. The (d)-() denote the results of RGANet (ours). EyeCoD You et al. (2023)) LLI_T (Pan
et al. (02 LOINet (Yin al. (2022)) MSCAF-Net (Liu al. (2023)), OCENet (Liu et al.
(2022) ) and ZoomNet (Pang et al. (2022)). ). The (c) shows label maps corresponding to lensless
imaging measurements (a) and underlying scenes (b).
1gi
[m]
Figure 6: Qualitative comparisons of the ablation study different configurations. The (d)-(m) denote the configurations #1 #10, respectively. The (n) the result with our full model (RGANet), and (c) is the label map corresponding to the lensless imaging measurement (a) and the underlying scene (b) (see Table for configurations indexed by IDs).
compared methods for COD in lensless imaging. Overall, our method achieves balance between detection performance and computational complexity.
4.4 ABLATION STUDIES
The Effectiveness of RGM. Table 2 (44, #5, #6) reveals significant boost in detection performance when integrating FCE into RGMs (#5, #6), relative to baseline (#4). This enhancement is corrobo- rated by Fig. 6 (g)-()), illustrating more complete object regions and finer details due FCE integra- tion. These outcomes highlight the crucial role of frequency cues in advancing the COD framework for lensless imaging. Additionally, Table presents ablation studies frequency adaptive selection within FCE. emphasizing the significance of balanced low- and figh-frequency utilization. An op- timal ratio (r 5) consistently outperforms other configurations underscoring the necessity of frequency adaptation for effective RGANet application in COD. Ta- ble 2 (11, #7, #11 1) shows that reducing any RGM degrades performance, emphasizing their role in ensuring precision and reliability. Visual comparisons in Fig. (d) and (j) further confirm that fewer RGMs result in lower performance and visual quality.
1. r 7. and exclusions),
The Effectiveness of RA. The evaluation results presented in 2 Ours) highlight the signif- icant enhancement COD performance achieved through the incorporation the RA. Conversely, the exclusion of RA results in substantial decline in performance. Furthermore, (6 and (n), RA effectively enhances local details, illustrating its ability to amplify concealed object features. This observation validates RA's capacity to focus local intricacies, allowing the
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Table 2: Quantitative comparisons of the ablation study different configurations. Best results highlighted in red.
1tst RGM
2-nd RGM
Test- East-Easy
Test-Hard
ID
OFE
RA
HIF
FCE
SIE
SFFF
FCE
SIE
SEFF
F3
1
M 4
F8" f
M
#1
0.509
0.259
0.468
0.306
#2
0.624
0.163
0.511
0.182
#3
V
0,651
0.139
0.557
0.158
#4
0.682
0.134
0.564
0.153
#5
0.721
0.122
0.596
0.147
#6
0.718
0.125
0.592
0.145
#7
0.729
0.116
0.601
0.142
#8
0.795
0.087
0.672
0.116
#9
0.756
0.106
0.617
0.134
#10
0.423
0.382
0.392
0.361
#11
0.631
0.157
0.539
0.162
Ours
0.815
0.079
0.705
0.098
Table 3: Quantitative ablation analysis of frequency adaptive selection in FCE. Best results are highlighted in red.
Test-Fasy
Test-Hard F5
ID
Configuration
Fg
M4
M4
11
w/o. pH w/o. PL
0.732
0.109
0.608
0.139
# 12
0.769
0.102
0.656
0.122
#
r
0.774
0.098
0.662
0.114
# 14
r
7
0.782
0.091
0.675
0.103
Ours
Full model (r = 5)
0.815
0.079
0.705
0.098
network to extract more valuable information from specific regions and contributing to the overall improvement in COD performance.
The Effectiveness of HFD Module. The evaluation results presented in Table 2 (#8, Ours) un- derscore the superior performance of our proposed HFD module terms of COD accuracy. The visualizations Fig. 6 (k) and (n) provide additional insights, illustrating that the HFD module effectively integrates information from the RGMs compensate for missing features. This tegration leads to more refined and visually enhanced results, highlighting the capacity of the HFD module to improve the overall effects of COD.
The Effectiveness of OFE Module. The results presented in Table 2 (#10, Ours) underscore the pivotal role of the OFE module COD for lensless imaging. Specifically, the removal of the OFE module leads to decline in COD performance, while its inclusion enhances COD accuracy. Additionally, a visual comparison in Fig. (m) and (n) further supports these findings, emphasizing that the absence of the OFE module hampers practical performance, while its inclusion enables accurate and meaningful generalization of results. This observation suggests that incorporating the OFE module provides a rrihh source of underlying semantics that significantly benefits the COD task.
CONCLUSION
This paper aims to solve the challenge of accurate COD on lensless imaging measurements by proposing a region gaze-amplification network (RGANet). the RGANet, we incorporate fre- quency and spatial cues by RGMs for driving reasoning performance and introduce the RA embed- ded in the two RGMs to magnify potential concealed objects for refining final results. Furthermore, present the first dataset for COD lensless imaging measurements, which serves a bench- mark for future research. Experimental results demonstrate the superiority the RGANet over existing methods and highlight its potential in advancing the community of lensless imaging.
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6 ETHICS STATEMENTS
This study advances lensless imaging for concealed object detection (COD) with potential applica- tions in medical imaging, security screening, and camouflage identification. We ensure responsible research practices by adhering to ethical considerations. Our work does not involve personal or sensitive data, and the contributed dataset synthetically generated or collected under controlled conditions to avoid privacy concerns. While the method may benefit medical and security applica- tions, it is intended purely for scientific advancement, and any deployment sensitive areas should comply with ethical guidelines and regulatory approvals. We also strive to mitigate biases in our dataset and model to ensure fairness and generalizability across imaging conditions. By following these principles, we aim contribute positively to computational imaging and COD research while ensuring ethical and responsible development.
7 ACKNOWLEDGEMENT
We would like to acknowledge Centre for Integrated Circuits and Systems (CICS) and School of Physical and Mathematical Sciences (SPMS), Nanyang Technological University (NTU), Singapore for their support in this research. Additionally, we appreciate Jingyu Yang, Kun Li. and Huanjing Yue from Tianjin University. China for their insightful discussions during the experiments, which provided valuable guidance for the successful progress of our work.
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A APPENDIX
A.1 DATASET
To reduce data costs, combined simulated and real data build the dataset for model training- The data generation process is shown in Fig. 7 The collection of real data requires adherence to the following experimental configuration:
(I) Distance between the PHIatCam and the display: The PHlatCam was positioned 42 cm from the display throughout all real-capture experiments. This distance was carefully selected to optimize image capture, considering the camera's 's field of view and resolution. This configuration remained consistent during both training and testing phases, ensuring uniform alignment of camera and mon- itor pixels.
(2) Display specifications:
Model and Type: The display used was Dell S2425HS, which is an LCD screen.
Size: The screen size was 24 inches, with resolution of 1920x1080 pixels.
Additional Notes: The image was resized via bicubic interpolation to fit the largest central square on the monitor. The white balance for PHlatCam was calibrated using the automatic white balance setting of the PointGrey Flea3 camera, determined when an all-white image was displayed on the monitor. The exposure time was governed by automatic mode of camera, with gain fixed 0 dB.
Data collection is completed based on the aforementioned processing. Based on these, we divided the collected datasets into separate sets for training and testing. For training, we randomly selected 2060 pairs from DLCOD and combined them with SLCOD, creating training set with 3917 pairs. For testing, the remaining DLCOD data was split into two sets based on verification difficulty: Test- Easy with 220 pairs and Hest-Hard with 320 pairs. Figure g presents examples from these datasets.
A.2 COMPARISONS WITH
REA.TTERRRRRROOSTRUUTIOOO STRATEGY
To investigate the effect of the derection-affer-rcconstut strategy, we employ the state-of-the- art FlatNet (Khan et al.] (2022)) for reconstruction. Subsequently, apply EyeCoD ( (You et al. 2023 ) LLI_T (Pan et al. (202 LOINet (Yin et al. (2022)), ) MSCAF-Net (Liu et al. (2023)) OCENet (Liu et al. (2022)). ) and ZoomNet (Pang al. (2022) for COD. For equitable comparisons, our method replaces the OFE module with FlatNet. In Fig. 9 (b), the reconstructed image by FlatNet
closely resembles the underlying scene in Fig. 9 (a).
When performing COD based on the reconstructed results, our method (Fig. (d)) consistently out- performs advanced COD methods such as MSCAF-Net al. (203)) (Fig. (h)), OCENet(Liu
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Simulation
Select from COD dataset such as CAMO, CHAMELEON CODIOK, and NC4K
F
F
SLCOD
L Lensless Imaging Simulation
Training dataset
DLCOD-
Select from display captured dataset by PHlatCam
Train
DLCOD
T Test-Easy
T l'esting dataset
Eiseg Anooaation Software
Annolation
Test-Hard
Figure 7: The formation process of datasets for performing COD of lensless imaging measure- ments.
Training dataset
Testing
SLCODD
DLCOD-Train
Test-Easy
Test-Hard
Figure 8: Examples from datasets. Each row, from top bottom, represents lensless imaging measurements, underlying scenes, label maps, and annotation visualizations (i.c., label maps over- laid on underlying scenes for annotation clarity).
et al. 2022
(Fig. g (i)), and ZoomNet Pang et al. (2022) (Fig 9 (i)). Additionally, we provide Compared with the re- paradigm yields improvements of up to 10% for all compared methods in terms of F the cost of huge computational complexity. These findings validate the promising potential of direct COD lensless imaging measurements.
quantitative results of the detection-affte recfostrrcoo strategy Table 4 sults in Table the
A.3 COMPARISONS WITH LATEST COD METHODS
To further validate the superiority of our method, select two recent methods (FEDER He et al. (2023) and FSPNet Huang et al. (2023)) and conducted comparative experiments based on the set- tings and open-source code provided earlier. The corresponding qualitative and quantitative results
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(c)
(e)
Figure 9: Qualitative comparisons of COD using the deteciionaffee-rrconsittiii strategy Test-Hard dataset. The (d)-(j) denote the results RGANet (ours), EyeCoD (You et al. (2033) ). LLI.T (Pan et al. (2021a)). LOINet (Yin et al. (2022)), MSCAF-Net (Liu et al. (2023)). OCENet (Liu et al. (2022)). and ZoomNet (Pang et al. (2022)). The (a) depicts underlying scenes, (b) shows re-
constructions by FlatNet (Khan et al. (2022 and (c) illustrates label maps.
Table 4: Quantitative results of COD for lensless imaging measurements using the detection-after. reconstruction strategy on Test-Easy and Hest-Hard datasets.
Test-
Test-Hard
Method
FLOPs (G) # Param (M)
F M
Eg S.
13 M
S.
FlatNet + ByeCoD
204.27
86.32
0,810 0.085 0.823 0.807 0.708 0.091 0.832 0.794
FlatNet + LLLT
164,25
76.63
0,836 0.063 0.887 0.859 0.729 0.075 0,847 0.835
FlatNet + LOINet
126.32
84.71
0.843 0.054 0.897 0.868 0.751 0.063 0.866 0.827
FlatNet + MSCAF-Net
182.94
89.72
0.831 0.071 0.889 0.851 0.737 0.078 0,856 0.804
FlatNet OCENet
133.22
114.41
0.829 0.057 0.876 0.853 0.741 0.071 0,852 0,816
FlatNet + ZoomNet
159.31
91.98
0,847 0,051 0.903 0.871 0.752 0.059 0.869 0.831
FlatNet Ours
48.62
39.45
0.815 0.079 0.896 0.834 0.705 0.098 0,845 0.770
As evidenced by the results, the comparison methods show sig- nificantly lower performance in terms object detection completeness, with the quantitative metrics further supporting this conclusion.
are presented in Fig. 10 and 5
A.4 COMPARISONS WITH STATE-OF CEATEOF-THE-AAE THE-ART COD METHODS ON REAL NATURAL SCENE
To verify the generalization ability in natural scenes, we conduct additional experiments using dataset that captures concealed scenarios natural environments, free from screen-based biases. This dataset, consisting of 34 pairs of lensless imaging data, reconstructed scenes, and ground truths, covers a broader range wavelengths, allowing us to evaluate the model performance in unfiltered, real-world conditions. The results shown in Fig. 11 highlight the effectiveness and robustness of our method in real-world conditions, extending its capabilities beyond screen- screen-based data.
Table 5: Quantitative results by latest COD methods. Best results are highlighted in red.
Method
Test-Easy
Test-Hard
F1
ML
Eg 6
S. 1
F 1
Mi
Eg 1 Sa 1
FEDER 0.741 0.113 0.837 0.797 0.539 0.159 0.746 0,712 FSPNet
0.584 0.131 0.757 0.724 0.815 0.079 0.896 0.834
0.757 0.105 0.845 0.816
Ours
0.705 0.098 0.845 0.770
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Measurements
Scene
FEDER
FSPNet
GT
Figure 10: Qualitative results with the latest COD methods, such as FEDER He et al. 2023 FSP- Net Huang al, (2023), and ours.
Measurement
Reconstraction
Labe
Ours
DGNR
MICINN
PUNet
MGL
OCENM
Figure 11: Qualitative comparisons between our method and state-of-the-art COD methods on real natural scenes.
A.5 DISCUSSIONS AND LIMITATIONS
Our method detects concealed objects for lensless imaging with high precision as illustrated in the above results. However, method struggles reason well in cluttered backgrounds and small objects, as illustrated in Fig. [2] Specifically, concealed objects implied the above scenes suffer from incomplete detection and incorrect object location by our method and advanced methods, such as EyeCoD and ZoomNet. In the future, we will dedicate more efforts improving the COD accuracy in these cases.
with low contrast, extremely
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(b)
(c)
(d)
(e)
Figure 12: Illustration of failure cases. Examples are presented from (d) our method and state- of-the-art methods, such as (c) EyeCoD and (l) ZoomNet. The (a)-(c) denote lensless imaging measurements, underlying scenes, and label maps, respectively.
18


Reviews:
[{"review_id": "o1zJQPepJh", "reviewer": "Reviewer_pe4y", "summary": "The authors introduce a new method for detecting concealed objects using a lensless camera, the Region Gaze-Amplification Network (RGANet), which progressively enhances concealed object detection through well-crafted feature extraction and amplification techniques. A novel real-capture dataset is proposed for training and evaluate the proposed method.", "questions": "Please refer to the *Weakness* section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written, and the experimental results are compelling. The proposed new real-capture dataset DLCOD will help further research in this field. Additionally, the authors have discussed the limitations of their proposed method in the appendix.", "weaknesses": "There are some aspects that could benefit from further clarification and enhancement:\n\n1. Additional details about the setup of the real-capture experiments would enhance the reproducibility and understanding of the method. Specifically, could the authors provide information on the distance between the PHlatCam and the display, as well as the display's specifications (e.g., size, model, and whether it is an LCD or OLED)?\n\n2. Although the model was trained on a real dataset, the data was captured from a display screen. Given that lensless cameras may capture a broader range of wavelengths than standard RGB cameras, will using a screen-based dataset introduce potential bias? The model may be less effective in real-world conditions where wavelengths are not limited to the three produced by RGB displays. It would be beneficial for the authors to conduct additional experiments using non-display-based scenes to validate the model's performance in more natural, unfiltered conditions (qualitative evaluation is not required). If this is not feasible, further discussion of this limitation could be included.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8jA57mGwPG", "reviewer": "Reviewer_EKVY", "summary": "The authors aim to address concealed object detection (COD) with lensless imaging systems. They propose a network for COD leveraging spatial and frequency feature enhancement and fusion. They also annotate 2600 paired images from the Display Captured Dataset to build a new dataset for COD with lensless imaging systems. However, the authors should design a special network for this task by considering the characteristics of the lensless camera, and clarify the details of the proposed dataset.", "questions": "1. Does the performance improvement come from the large parameters and computational cost?\n2. Error in Figure 2, where the sigmoid function is directly fed to addition without any input in PVT.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. A new dataset for COD with lenless imaging system.\n2. Good performance for a new setting.", "weaknesses": "1. Straightforward method with limited novelty. The authors do not analyze the challenge for COD with lensless cameras. The main difference in design for the lensless cameras is the optical-aware feature extraction, but it refers to [1]. In addition, the main module of the proposed method is the spatial-frequency enhance module, which directly uses the idea of existing works for COD [2, 3].\n\n2. Insufficient experiments. The authors should compare with sota COD methods, such as [2,3,4]. Moreover, they should compare with the two-stage methods (Lensless imaging methods combined with COD methods). With the large parameters and computational cost, the two-stage methods may be more lightweight.\n\n3. Details of the proposed dataset. The authors should provide a detailed analysis of the proposed dataset to clarify the difference between the dataset from [5], since some samples shown in the paper are the same as samples in [5].\n\n[1] PhlatCam: Designed Phase-Mask Based Thin Lensless Camera. TPAMI2020.\n[2] Frequency perception network for camouflaged object detection. MM2023.\n[3] Camouflaged object detection with feature decomposition and edge reconstruction. CVPR2023.\n[4] Feature shrinkage pyramid for camouflaged object detection with transformers.CVPR2023.\n[5] Inferring Objects FromLensless Imaging Measurements. TCI2022", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FZw4vc2sD0", "reviewer": "Reviewer_ZAok", "summary": "The authors propose a region gazeamplification network (RGANet) for progressively exploiting concealed objects from lensless imaging measurements.", "questions": "See Weaknesses for details.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. PHlatCam dataset is semantically labeled and contributes to the corresponding dataset as a benchmark and extensive experiments.\n2. investigate the detection of concealed objects in lensless imaging scenarios.", "weaknesses": "1. The use of RGM twice in the network is confusing. The ablation experiment only studies the effectiveness of the combination of internal modules under the setting of RGM twice. It sounds more reasonable to input RGM after I_OFE passes through RA.\n2. Under the condition of lensless cameras, the author studies concealed object detection. Such a combination of tasks makes people doubt whether its real application scenarios are wide. Why not study more common object detection tasks?\n3. The design of the entire network framework and internal modules is relatively ordinary. Basically, it is based on existing network modules with certain modifications, giving people a feeling of an A+B combination.\n4. The format and layout are uncomfortable, for example, the formulas in the paper have larger line spacing. In addition, the figures in the paper are not beautiful enough, and the color matching is abrupt.\n5. From the ablation experiment result #10 in Table 2, we can see that OFE is the most important core part of the network. I think OFE+encoder-decoder can achieve good results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)