High Dynamic Range Imaging with Time-Encoding Spike Camera
Authors: Zhenkun Zhu
Ruiqin Xiong
Jiyu Xie
Yuanlin Wang
Xinfeng Zhang
Tiejun Huang
Venue: NeurIPS 2025 poster ()
Keywords: Spike Camera
High Dynamic Range
Imaging
Primary area: applications

Abstract: As a bio-inspired vision sensor, spike camera records light intensity by accumulating photons and firing a spike once a preset threshold is reached. For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and a limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing a spike-firing counter (SFC) to count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires an SFC with a higher bit depth, causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.e., an overflow) is reached. To encode time information with as few bits as possible, instead of directly utilising a timer, we leverage a periodic timing signal with a higher frequency than the readout signal. Then the recording of overflow moment can be transformed into recording the number of accumulated timing signal cycles until the overflow occurs. Additionally, we propose an image reconstruction scheme for TE spike camera, which leverages the multi-scale gradient features of spike data. This scheme includes a similarity-based pyramid alignment module to align spike streams across the temporal domain and a light intensity-based refinement module, which utilises the guidance of light intensity to fuse spatial features of the spike data. Experimental results demonstrate that TE spike camera effectively improves the dynamic range of spike camera.

High Dynamic Range Imaging with Time-Encoding Spike Camera
Zhenkun Zhu' Ruiqin Xiong'" Jiyu Xie² Yuanlin Wang' Xinfeng Zhang' Tiejun Huang'
'State Key Laboratory of Multimedia Information Processing,
School of Computer Science, Peking University
Shanghai Radio Equipment Research Institute, Shanghai, China
School of Computer Science and Technology, University of Chinese Academy of Sciences edu.edu. (zkzhu, wangyuanlin]@e= pku. edu. cn, {rqxiong, t.jhaang @pku. edu. cn, xjy6460mail ustc. edu. en, xfzhang@ucas ac. cn
Abstract
As a bio-inspired vision sensor, spike camera records light intensity by accumulat- ing photons and firing a spike once a preset threshold is reached, For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing spike-firing firini counter (SFC) count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires SFC with a higher bit depth. causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.c., an overflow) reached. To encode time information with as few bits as possible, in- stead of directly utilising timer, we leverage periodic timing signal with higher frequency than the readout signal. Then the recording of overflow moment can be transformed into recording the number of accumulated timing signal cycles until the overflow occurs. Additionally, we propose an image reconstruction scheme for TE spike camera, which leverages the multi-scale gradient features of spike data. This scheme includes a similarity- streams across the temporal domain and light intensity- based refinement module, which utilises the guidance of light intensity to fuse spatial features of the spike data. Experimental results demonstrate that TE spike camera effectively improves the dynamic range of spike camera. The source codes and datasets are available at https://gtthbbbcmm/////11111/33
pyramid alignment module to align spike
Introduction
In applications such autonomous driving and unmanned acrial vehicles, high-speed and high- dynamic-range (HDR) scenes (26) 28) frequently occur. Some specialised sensors designed for these scenarios have a bit depth exceeding 20 accommodate dynamic ranges of 120dB or even higher. How to achieve effective imaging in high-speed and HDR scenes has become key challenge. Conventional digital cameras usually require static scenes to achieve nigh-quality imaging results. as object motion during the exposure time leads to motion blur. Consequently, the applicability of conventional cameras in high-speed scenes limited.
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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As a recently invented retinal-based camera, spike camera 19 60: 65
records light intensity by continuously accumulating photons and firing a spike when reaching a preset threshold. With readout frequency up to 40k Hz, spike camera demonstrates significant advantages in capturing high-speed scenes, However, its support for HDR is still insufficient, as it can only indicate whether spike is fired at readout interval, i.e., whether a certain amount of photons have arrived within the interval. In HDR scenes [22] 12: 25 54, 53), those high-light regions may trigger multiple spikes within readout interval, while spike camera can only store and read out single spike per readout interval. This prevents spike camera from accurately recording the light intensity in high-light regions.
To address these challenges, Zhu et al. 667 propose multi-level (ML) spike camera, which incorpo- rates a spike-firing counter (SFC) to count the number of spikes. At the readout moment, ML spike selects spike symbol as output based on the spike count. By mapping different brightness levels to distinct spike symbols, MI. spike camera achieves more precise encoding of light intensity.
However, in ultra-HDR scenes, the spike count can become excessively large, making counting and representation costly and inefficient. To this end, we propose time-encoding (TE) spike camera. Similar to ML spike camera, TE spike camera incorporates an SFC count spikes. TE spike camera sets a maximum spike count (overflow) per readout interval. Once the overflow occurs, TE spike camera stops spike counting and instead uses the time required reach the maximum spike count to represent the light intensity. To achieve the goal of encoding time information with as few bits as possible, instead of directly utilising a timer, we exploit two periodic clock signals that inherently exist in the spike camera: the readout signal and the figher-frrquency timing signal. Since the total number of timing signal cycles within each readout interval can be predetermined based on the frequency ratio between the two signals, the recording of the overflow moment can be transformed into recording the number of accumulated timing signal cycles until the overflow occurs.
Reconstructing high-quality images from TE spike streams is one of the key tasks for TE spike camera. To fully exploit the temporal-spatial information of TE spike streams and mitigate adverse effects, such as photon shot noise and quantisation noise, we propose an encoder to extract texture and gradient features from the spike streams across different receptive fields. We propose similarity- based pyramid alignment module to align the spike streams among temporal domains in a coarse-to trrsseett-ffin manner. Moreover, a light intensity- -based refinement module is proposed to utilise the light intensity to guide the fusion of spatial features in the spike streams. Experimental results demonstrate that TE spike camera effectively enhances the dynamic range of spike camera, and the proposed reconstruction method outperforms other methods, achieving superior results.
2 Related Work
HDR Imaging. For conventional digital cameras, the most straightforward approach to increasing dynamic range is to enhance the full well capacity of pixels [44, 34;44] Multiple conversion gain techniques [39 42 18 extend dynamic range by applying high conversion gain for low-light regions and low conversion gain for high-light regions. Multi-exposure imaging [4 56 49] captures the same scene with different exposure durations and then merges these samples to construct an HDR
image. The to-saurattion technique [32 20 27 estimates the intensity of saturated pixels by recording the time which pixel saturation occurs, thus extending the effective dynamic range. Other technologies such as single-photor avalanche diodes (SPADs) (37 36 38 and lonear-logaritmiic
pixels 30 23 have also been employed to improve the dynamic range of image sensors.
HDR Reconstruction for Neuromorphic camera. Neuromorphic camera can be mainly divided into 48 33 and spike camera (59 61 51 63
event camera (57 45
50 10 & 64
46 47].
Zhang et al. [58] design an encoder-decoder network, which combines spiking neural networks (SNNs) and convolutional neural networks (CNNs), effectively generating clear visual images, even for occluded or obscured targets. Zou et al. (8) focus on the reconstruction of high-speed HDR videos based on event streams. They introduce convolutional recurrent neural networks and employ temporal consistency loss to ensure accurate and consistent reconstruction of high-speed events in HDR video sequences. Han et al. [16] introduce the Neurlmg- framework, which uses HDR intensity images from neuromorphic cameras to guide traditional RGB images in the luminance domain, enhancing HDR output. Expanding upon this, Han et al. [15] develop NeurImg- HDRR+ which extends their method to support HDR video generation and high-resolution reconstruction.
2

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Intensity Representation Primitive Spike
Recumulii Photons
Intensity Representation Spike
Timm
Time
(a) Representation Light intensity
Intensity Representation
Encoding Spike
Figure 1: Mechanisms of light intensity representation with neuromorphic camera. (a) Neuromorphic camera uses the ratio of accumulated photons to time, i.e. the photon arrival rate, to represent light intensity. Two commonly adopted strategies are: (1) fixing the integration time and recording the amount of accumulated photons, and (2) fixing the amount of accumulated photons and measuring the time required to reach that amount. (b) Each spike represents a fixed amount of photons, this mechanism uses the spike interval represent intensity. (c) This mechanism uses multi-level spike symbols to represent different amounts of photons. (d) This mechanism represents intensity by recording the time required for photon accumulation to reach specific photon amount.
Comparison with Event camera and SPAD. Unlike spike camera, which continuously records light intensity, event camera only records the changes of light intensity, so difficult for event camera to perceive static objects. Cao et al. (3] focus on low- and moderate--rightness regimes (e.g., room light, outdoor sunset), where random fluctuations in the photon arrival process are the dominant source of noise. However, in high-brie brighttisssesss environments (e.g., outdoor daylight). leakage noise events become more prevalent, which are not modelled in their work. Therefore, the performance of such approaches in HDR scenarios remains uncertain. He al. 117 propose artificial micro- maccade-enhancer event camera that actively senses static scenes using rotating wedge prism in front of the event camera. While effective in enabling the perception of static objects, this mechanism may face challenges during the initial driving phase, where both static and fast-moving objects are present. Moreover, the additional mechanical components and the resulting complex data structure may introduce higher energy consumption and increased computational burden.
SPAD sensors estimate the total number of incident photons during interval by counting the number arrived photons within the exposure time. Sharma et al. [40] and Liu et al. [29] implicitly rely the assumption that the number of arrived photons during the exposure time does not exceed the maximum countable capacity of the SPAD. However, when the incident photon count surpasses certain threshold under high-illuminance conditions, conventional SPADS encounter difficulties in accurate photon counting. This limitation leads to image white-out, where bright regions are saturated. Moreover, the avalanche multiplication process SPADs not only amplifies the signal but also inherently amplifies the noise, leading to excessive noise levels that can severely compromise the overall imaging quality, particularly under high-noise conditions.
3 Motivation
Two Approaches for Intensity Representation. Neuromorphic camera uses the ratio of accumulated photons to time, i.c. the photon arrival rate, to represent light intensity. As illustrated in Fig. [a), two common approaches
typically adopted. The first approach accumulates the arrived photons N₂ over fixed time interval T. yielding photon arrival rate of N The second approach records the time duration Ti required for the accumulated photons to reach a fixed amount Ng. resulting in photon arrival rate of No 7.
Intensity Representation via Primitive Spikes. A primitive mechanism involves accumulating photons and comparing them with predefined threshold 0. Once the threshold is reached, spike is fired, setting up spike flag, and the accumulated photons are immediately released. The spike flag periodically read out output. In this mechanism, shown in Fig. (b), each spike corresponds to

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fixed amount of photons, i.e. No is fixed. This mechanism is simple version of the second approach, where the time between spikes, that is, the spike interval, reflects the time required accumulate No photons. Consequently, the spike interval inversely correlates with light intensity and becomes shorter as the intensity increases. Denote a readout interval as T,- In theory, the spike interval can be any integer multiple of T.. However, in high-speed HDR scenes, the light intensity within a spike interval may vary significantly, and an excessively long spike interval can lead to degraded image quality. Assuming that the maximum acceptable spike interval is N, the intensity I that can be represented by this mechanism is given by:
No
MENt n N.). nT,
Intensity Representation via Multi-Level Spikes. However, when the light intensity becomes very high, specifically when the intensity 12 > 7 multiple spikes would be fired within a readout interval. In this case, the primitive mechanism fails. To address this, a multi-level spike mechanism is introduced. This mechanism not only generates spikes but also counts them. At the readout moment, the spikes that have been fired but have not yet been read out will be encoded and output as spike symbol with an amplitude value. As illustrated in Fig. (c), the height of each spike symbol corresponds to the number of spikes fired within the readout interval. This mechanism combines elements of both the first and second approaches: it uses the spike count per readout interval to represent high-intensity regions, while relying on spike intervals to represent low-intensity regions. Assuming that the bit depth of the counter is B, the intensity 12 that can be represented by this mechanism is given by:
12 bNg nT,
I b E n n No, 2
Intensity Representation via Time-Encoding Spikes. However, in ultra-HDR scenes, the number of fired spikes can become excessively large. requiring spike counter with a high bit depth. This poses a significant challenge for maintaining compact pixel sizes. To overeome this, time-encoding mechanism is introduced. Specifically, by defining maximum spike count Prmxx within a readout interval, once the spike count reaches Pimxx the representation of light intensity shifts from spike count to the time Ta required to reach Pmmx- TM is then encoded and output, providing an alternative representation of intensity through time information. The intensity 73 that can be represented by this mechanism is given by:
bN
no overflow,
nTr
13
(3)
Poux No
Tr
overflow,
T,
TM
where n, b NT, S No. b < P Pmaxs and 0 < TM TT.
Time-Encoding Spike Camera
4.1 Working Mechanism
The working mechanism of time-encoding (TE) spike camera is shown in Fig.
Spike Generation. Each pixel of TE spike camera independently captures the incoming photons and converts them into electric charges for accumulation. These charges are continuously accumulated by the integrator. Once the accumulated electric charge, denoted as A(t), reaches a predefined threshold 0, the system generates a spike to represent a spike-firing and releases the accumulated electric charges under the enable operation of periodic timing signal S,, restarting new "integrate-and anngraate-nddffiee and-fire" cycle.
Spike Counting and Time Encoding. TE spike camera introduces spike-firing counter (SFC) to count the number of fired spikes. To record the time required for the spike counting to reach Pmax (i.e., overflow), a straightforward approach is to introduce a timer. However, encoding timer value would consume a significant number of bits. As shown in Eq. 3 the key to time encoding lies in obtaining the ratio between TM and T,- T, is controlled by the readout signal S,. Since multiple integrate-ane and-fire cycles may occur within a single readout interval, the frequency of Sn is set to

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INC
Clock-Cycle Counter
Overflow Flag
"0" "1
Control
Overflow
Photon Receptor
A(t)
INC
Spike-Firing
Integrator
Comparator
Comparator
Counter
Spike Generation
RESET
Spike Counting und Time Encoding
S.: Readout Signal
Overllow
Spike-Firing Counter
OF
"0"
Readout
8
BESCJ
OF: Overflow Flag
OF
OF
Readout Circuit
Overflow Flag
S,: Timing Signal
Counter Number
S,
172. cycles
Clock-Cycle
RESET
Control
Time Encoding
cycles
Counter
OF
S
Readout Interval
Figure 2: Working mechanism of time-encoding (TE) spike camera. Each pixel of TE spike camera continuously accumulates photons and fires spike when preset threshold is reached. Based on the overflow flag, TE spike camera decodes the light intensity with spike number or time information.
be significantly higher than that of S,. Therefore, the problem of computing the ratio between TMr and T, can be transformed into calculating the ratio between the number S,, clock cycles until the overflow occurs, and the total number of S₁, cycles within single readout interval. To this end, we introduce a clock cycle counter (CCC) to count the number of S,, cycles. Since both Sh, and S, periodic signals, the total number of S₁ cycles within readout interval can be determined in advance, and we denote this number as a. During a readout interval, if the spike count exceeds the maximum value that the SFC can store, overflow occurs, setting up the overflow flag and making both the SFC and the CCC stop counting. If no overflow occurs, the SFC count spikes throughout the entire readout interval.
In the practical implementation, to facilitate manufacturing, all pixels can share single CCC, and each pixel maintains dedicated local register to store the number of S₁, cycles provided by the CCC.
Readout. If the overflow flag is "0", the value stored the SFC read out along with the overflow flag, otherwise, the value in the CCC is read out with the overflow flag. Afterwards, the SFC, CCC and overflow flag are reset prepare for the next process.
4.2 Decoding of Spike Data
Suppose the output is d + 1-bit binary number. If the overflow flag is "0", the remaining d-bit (i.e., the lower bits) binary data can be directly decoded into decimal number, representing the total number of spikes fired during the readout interval. Then the intensity can be estimated with Fq. [ If the overflow flag is "1", the remaining d-bit data instead represents the time information. First, the d-bit binary data is converted to a decimal number which represents the number of clock cycles of Sp, until the overflow occurs. Since the total number clock cycles of Sj, within a readout interval a, the Pe. in Eq. can be approximated as a
4.3 Dynamic Range
According to Eq. 3. the minimum representable light intensity Igii N₂ N.T.- The maximum repre- sentable light intensity is jointly determined by the bit depths of the SFC and the CCC. Suppose the bit depths of the SEC and CCC are B1 and B2, respectively and define overflow occurring when the overflow flag of the SFC becomes "1", i.e. PAEx = B1, Then the maximum light intensity be expressed as Tmax N (2Bz 1). In this case, the dynamic range of TE spike camera Drg = 20 log (I Imin) = 20 (2 (2 1)N2). From Eq. 2 we can obtain the dynamic range of MI. spike camera DAL. = 20 (02 1)N.). When we set B, + Bg > B, can obtain DTE DML-
5

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Ixl Cusr
Pustion
MSFRG
Lound,
Sxi Sarr
LIR
7X7 Goer
MSFFC
SMA
Txl Care
Sobet X
Cane
Sabee
MSFIG
Lid Cook
Lapladiz
OUttmillrity based Peramid Algnasent
fellaght inreusty based Antinemant
Phe net
M
DCN
Capilatia
Lauer
DCNS
Lawer
Radaap
17
DCNS
OffICE
Mask
Nosional Ensoiig
Figure 3: Architecture of the proposed reconstruction model. The MSFEG module extracts multi- scale features as well as gradient information to facilitate the following alignment process. The SPA module aligns the spike streams among temporal domains in a coarse-to-fine manner. The LIR module utilises the guidance from light intensity to fuse the spatial features of the spike streams.
5
Image Reconstruction for Fime-Encoding Spike Camera
5.1 Overall Architecture
The overall architecture of the proposed reconstruction method is shown in Fig. 3 For the given TE spike stream S "X segment S into five spike sub-streams {S 3 to reconstruct the scene at time to. where i represents the time index. Each sub-stream S, is centered at moment t, with window radius w/, defined as:
(S(p, t
(4)
Here, is designated the key moment, while (4:)iE(-2 represent reference moments. We utilise an encoder extract multi-scale features as well as gradient information {F.) from these five spike sub-streams. To facilitate effective fusion, we design similarity-based pyramid alignment module to align the spike streams among temporal domains in a coarse-to-fine manner. Moreover, light intensity- based refinement module is proposed to utilise the guidance from light intensity to fuse the spatial features of the spike streams.
5.2 Multi-Scale Feature Extraction with Gradient
As shown in Fig. (a), employ convolution kernels of sizes 1, 3, and 7 for feature extraction, facilitating the capture of motion at varying scales through different receptive fields. The spike stream generated by TE spike camera comprises multiple spike symbols, where higher values correspond to stronger light intensity, thereby embedding rich gradient information. This gradient information crucial for precise motion alignment. To leverage effectively, apply horizontal and vertical Sobel operators, along with a Laplacian operator, to extract essential features from the spike stream. More information about gradient extraction be found in our supplementary.
5.3 Similarity-based Pyramid Alignment
To facilitate the effective fusion of reference features and the key feature, we employ pyramid structure to achieve coarse-to-fine feature alignment. Specifically, we propose similarity- pyramid alignment (SPA) module. As illustrated in Fig. 3 (b), we convolution layers to

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downsample the extracted features and construct a three-level pyramid. The key feature at to moment the (-th pyramid layer is denoted as F and the reference feature at moment at the (-th pyramid layer is represented as Fuet 2,1" First, F and Fret are concatenated and then passed through two
convolution layers generate the offset O2. and mask M33. Subsequently, Fidt is aligned F
by deformable convolutions (DCNs) [5 66]
kp)(p)
Σ W.
F + + OL3(P.Pi)) Mr, Pc),
where F.a denotes the aligned feature the third pyramid layer. The coordinate p (2,, y) denotes the center location, n refers to the number of sampling locations, wi represents the i-th weight, and p) denotes the i-th fixed offset.
To mitigate motion blur and potential artefacts in occluded regions, we propose motion bias predictor (MBP) module to evaluate the bias between F Fig and F The bias is then added to Fi, to obtain the refined feature F..3-
The offset Og,3 and mask M3, are propagated and fused across different pyramid levels through upsampling, as illustrated in Fig. (b). Similarly, the aligned feature F., is also propagated and fused between pyramid levels via upsampling. However, the feature information of F, may not be reliable. Additionally, downsampling during pyramid construction can lead to information loss, making the upsampled Fu potentially uncorrelated with F To mitigate this issue, we design a similarity predictor (SP) module, which takes the upsampled 7.3 and F as inputs and outputs a similarity feature s. This similarity feature is then used to guide the fusion of the DCNs-aligned F and the upsampled feature Fup A bias is also estimated to facilitate the fusion. The aligned reference features are concatenated with the key feature and passed through fusion module for preliminary integration and obtain F. More details about the SPA module can be found in our supplementary.
5.4 Light Intensity- based Refinement
After performing motion aligmment at different time instances, we further refine the preliminary integration result F using spatial domain information. Inspired by (2], considering that the texture details in regions with stronger illumination are generally more reliable, we propose a light intensity- based refinement (LIR) module, as shown in Fig. (c).
In fact, the values at different moments and locations in the spike stream linear with the light intensity. By averaging the spike stream along the temporal direction, we obtain a rough estimation of the light intensity at the key moment. This estimation is then concatenated with the spike stream to generate an intensity- feature I for refinement through a two-layer convolution network.
To capture long-range spatial correlations while maintaining computational efficiency, we apply multi-head self-attention network to extract the spatial domain information of F. F is reshaped into tokens X E IR FWWXX XC and X is further split into & heads:(X, X2, X For each head, three fully connected layers are applied to project X; into query Q, E #H key K, E B#wx & and value V, R "rx _ Then, we use the intensity- based feature to guide the computation of xCand
self-attention. detail, is reshaped into Y E IR IrW xC and split into & heads: {Y,, Y2...., ". Then the self-attention of each head can be calculated as:
K Q
att (Y; o s.oofmaax
)
%
where o means plement-wise multiplication and % R is learnable scaling factor. Subsequently. heads are concatenated to pass a fully connected layer and then plus a positional encoding e HW XC to produce the output tokens X, E HW xC", X, is then reshaped and the output feature F, E TR. H* xC is obtained. Finally, we use one convolution layer to adjust the channel number of F, and derive the final output F E 12 xx11
7

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Table 1: Comparison of quantitative results the synthesized HDM-HDR 2014 dataset means only one parsing branch is utilized).
MI. Recentmation
PSNR-y-
SSIM-
HDR-VDPT
HDR-VQM,
Reconstmation
SSIM-y
HDR-VDP
HDR-VQM,
MI.
0.61
3,06
TFI
0.33
THP_ML
0.70
THP_
3.412
0.700
Spk2IinNNeLMLL
0.779
0,499
SpC2lmnNeL.
7.892
0.357
BSF_ML.
0.782
0457
BSP_TH
MambaSpike
26.42
0.767
7,189
0,507
MambuSpike_ TE
28.91
0.828
7,802
0,400
MambuSpike
27.30
0,788
7,405
0.443
Ours
30,86
0,853
8.055
0.325
Table 2: Comparison of quantitative results the synthesized Kalantari 13 dataset means only
one parsing branch is utilized).
Reconstaction
PSNR-y+
SSIM-a
HDR-VDPT
HDR-VQML
Reconcimction
PSNR.u
SSIM-jc
HDR-VDP
HDR-VQMJ
THLML
24.55
1.222
TF_TE
3.882
TFP THP_ML
0.699
1,288
THLTE
Spk2ImgNet_Mi ML.
0.361
Syk22mg Net
30.29
9.587
0.138
BSF_ML
28.28
0.872
8.619
0.345
BSF_TE
31.74
0.937
9.586
0.132
MambaSpike
25.99
0.859
8.584
0.334
MambaSpike_1i TE
29.06
0.929
9.516
0,151
MambaSpike
27.85
0.874
8,699
0.325
Ours
33,65
0,943
9.606
0.139
Experiments
6.1 Spike Simulator
Based on ML spike camera and TE spike camera we propose, we design two corresponding spike simulators generate spike streams from video sequences. We convert them to grayscale to represent the light intensity in the external environment. The photoelectric conversion rate v) set to 0.7. Additionally, Poisson noise is added to simulate the shot noise during the photon arrival process. For ML spike camera, we utilise an SFC with a bit depth of 8. For TE spike camera, we utilise an SFC with bit depth of 4 and CCC with bit depth of 4. Suppose pixel value at the moment is (t), the threshold of ML spike camera is set to max(1)/2 and the threshold TE spike camera is set to (2 x (2 1)). More explanations of our settings can be found in our supplementary.
6.2 Dataset
We use part of HDM-HDR-2014 dataset [33 for training and the other part of HDM-HDR-22 2014 dataset, along with Kalantari13 dataset 22 for testing. Since Kalantari dataset does not have ground truth, we use the result of HDRFlow 52 (one of the SOTA HDR reconstruction methods) as the ground truth. More dataset settings can be found in our supplementary.
6.3 Implementation Details
During the training process, we randomly crop the spike streams spatial size of 96 96 and apply random horizontal and vertical flips for data augmentation. The network is trained for 60 epochs with a batch size of 4. We use the Adam (24] optimizer with parameter B1 0.9 and B3 0.999. The initial learning rate is set to le 4 and is halved every 10 epochs. To guide the training, we employ the L1 loss function to compute the difference between the estimated i(tu) and the ground truth ge (ln):
f = ==1(((t)/nn (to)ll
6.4 Comparison with ML Spike Camera
PSNR-u, SSIM-H, HDR- VDD-3 (11 and HDR-VQM 35 as evaluation metrics, where a means the HDR images are tone-mapped with y law 21] and we apply μ = 5000 in our experiments.
In our experiments,
To demonstrate the advantages of TE spike camera for image reconstruction, we adapt several reconstruction methods for ML spike camera in [67] to TE spike camera. These methods include two

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Sp
MamkuSpike
TFI TB
TFP TE
Spk3ImgNet TE
BSF TE
MarnhaSikk TE
Ground Truth
Figure 4: Visual comparisons of different reconstruction methods the synthesized HDM-HDR- 2014 dataset.
ML
TEP ML.
Spie2lng Net, ML
BSF ML
MambaSpake
Ours
TFLTE
TFP_TE TE
Spk2lingett_ TE
MambuSpike
Ground
Figure 5: Visual comparisons of different reconstruction methods on the synthesized Kalantari dataset.
training-fres approaches: TFL_ML BSF BL and MambaSpike.
ML and TFP ML, and three deep learning methods: Spk2ImgNet_MM ML,
The spike streams are segmented into slices input. Following the configuration in MambaSpike 677, each slice for the training-free methods consists continuous frames, while each slice for the deep learning methods contains 61 continuous frames. The average value for each sequence is computed, and the overall average across all testing sequences is used as the final experimental result.
Adapted from TFI ML and TFP ML, TFL and TFP utilise spike interval and spike- spike-firing firing rate a temporary window to predict light intensity, respectively. For three deep-learning methods, Spk2ImgNet ML. BSF ML and MambaSpike, we modify the input from ML spike streams to TE spike streams, resulting the modified versions: Spk2ImgNet TE, BSF TE, and MambaSpike TE. For fair comparisons, We retrain all three deep-learning methods on our training set. Unlike ML spike camera, which reads only the major part the SFC at the readout moment, spike camera reads all the data from the SFC. As result, there is no need to predict the remaining part of the SFC, thereby eliminating the need for multiple parsings of the spike stream. Consequently, we utilise one spike parsing in MambaSpike TE.

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Table 3: Ablation studies of the proposed method.
Case
MSFE
Gradient
DCNs
SP
MBP
LIR
PSNR- 1
SSIM-
25.68
0.680
26.35
0.697
29.54
0.818
/
/
30.04
0.828
v
30.32
0.824
6
V
30.86
0.853
As shown in Tab. [1] reconstruction methods based on TE spike camera significantly outperform those methods based on ML spike camera. Furthermore, the proposed method achieves the best overall performance in all the metries. The visualised reconstruction results in Fig. elearly demonstrate the advantages of TE spike camera-basee methods, particularly terms of texture detail preservation and motion alignment.
Testing results on the synthesized Kalantari 13 dataset are presented in Tab. 2 and Fig. 5
Methods
based on the TE spike camera exhibit a significant performance improvement over their ML coun- terparts, with the proposed method achieving the best results. This demonstrates that the proposed method exhibits strong generalization ability.
More visualised reconstruction results, analysis of noise influence and complexity analysis can be found in our supplementary.
6.5 Ablation Studies
We investigate the effects of the proposed multi-scale feature extraction with gradient (MSFEG) module, similarity-baase pyramid alignment (SPA) module, and light intensity- refinement (LIR) module. We use the synthesized HDM-HDR-2014 dataset for the ablation studies. The results presented in Tab. For the MSFEG module, Cases (1-2) highlight the effectiveness of the gradient features. For the SPA module, Cases (3-5) demonstrate the effectiveness of the deformable convolutions (DCNs), similarity predictor (SP) module, and Motion Bias Predictor (MBP) module, respectively. Finally, Case (6) illustrates the effectiveness of the LIR module.
7 Limitations
Current validation is limited to simulations, and additional challenges are anticipated in real-world applications.
8
Conclusion
We propose time-encoding (TE) spike camera, novel advancement over multi-level (ML) spike camera. TE spike camera incorporates an additional clock cycle counter (CCC) to record the time of spike counting. When the spike count reaches certain value, TE spike camera stops counting and retrieves the time required for the counting from the CCC. By computing the ratio of this time to the readout interval, the overflow moment can be represented. Furthermore, we propose an image reconstruction scheme for TE spike camera, we focus on the gradient features of spike data, and propose similarity-based pyramid alignment module to align the spike streams among temporal domains. Moreover, light intensity-based refinement module proposed to utilise the guidance from light intensity fuse the spatial features. Experimental results demonstrate that TE spike camera effectively enhances the dynamic range of spike camera.
Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation of China under Grant 62072009 and 22 1278777 and 62521007.
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A Supplementary
A.1
Module Design
A.1.1
Multi-Scale Feature Extraction with Gradient
The horizontal Sobel operator Gz, vertical Sobel operator Gy, and the Laplacian operator L applicd in our methods are:
0
1
G
+2
0
-2
+1
0 -1
+1
+2
+1
Gy
0
0
(8)
- -2
-1
+1
L
+1
-4
+1
+1
0
We assign the weights of these operators 3 x convolution kernels, and these weights remain fixed throughout the training process.
A.1.2 Similarity-base Pyramid Alignment
When 1, the operation in the 1_th layer of the similarity- based pyramid alignment module can be expressed as:
F.,(p) = Σ wi F. +p' +c.,(p,E')) Mc,(D.1))
1=0
1/ SP(Cat(F 1,441 Fllr)),
F = Conv(Cat(()) i,1, 8ia Fup (1+1)):
b MBP(Cat(F 2,1s B)rY))
F.,1 F + b,j
where Cat means concatenation and means element-wise multiplication.
The fusion module consists of two-layer convolution.
A.1.3 Motion Bias Predictor and Similarity Predictor
For the motion bias predictor (MBP) and similarity predictor (SP), to ensure the inference speed of the model, we adopt two-layer convolution and ReLU activation for realization. Even with this lightweight design, significant performance improvement can be observed. For different scenarios and requirements, more complex models can employed replacements.
A.2 Experiments
A.2.1 Spike Simulator
According to Eq. [ when the bit depth of the ML spike camera is 8, the threshold should be set to 2 However, to reduce output bandwidth, Zhu et al. (67] introduced a dual-buffer mechanism in the design of the ML spike camera, in which only the bit index of the most significant bit (MSB) of the SFC is output at each readout moment. As result, Eq. 2 is modified to:
bN nT, n, E EN S N. 2(B-1)3
In
(10)
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Therefore, the threshold is set to 2". Since the output mode of the ML spike camera is not the focus of this paper, we adopt Eq. for clarity and ease of understanding. In the experiments, ensure fair comparison, we use Eq. consistent with 67
A.2.2 Dataset Settings
For HDM-HDR-201 dataset and Kalantari dataset, we first extract the .hdr or .exr files from each sequence and convert the images to grayscale to simulate external light intensity. To facilitate subsequent data processing and neural network input, we identify the maximum pixel value within each sequence and normalise all the images in each sequence accordingly.
Following the settings of [67], partition HDM-HDR-201 2D2-2011- dataset into sub-sequences, each containing a continuous scene, with a maximum length of 400 frames and no repeated frames between sub-sequences. This results in total of 57 sub-sequences. We utilize 30 sub-sequences for training and reserve the remaining 27 sub-sequences for testing. To maintain the independence of the training and testing sets, sequeeeeeeess derived from the same original sequence are assigned exclusively to either the training or testing set.
For the training set, resize the original images from 1920 x 1080 to 240 135 to facilitate fast training. For the testing set, we crop the original images to resolution of 512 x 384.
A.2.3 Law
Following the settings of (21), / law is defined as:
T(Hy) log(1 (rHy)
(11)
log(1 + pt)
where Hy is the generated HDR image after normalized to [0, 1], and T is the tone-mapping operator and j the amount of compression. In our experiments, we set p 5000 to maintain consistency with standard settings.
A.2.4 Comparison with Event-RGB Hybrid Method
We compare the proposed method with the of the SOTA Event-RGB hybrid method HDRev [55] We retrained it on our own training dataset. Since HDRev generates color images, we converted them to grayscale for fair comparison. The results the synthesized HDM-HDR-2014 dataset shown Tab. The proposed method achieves the best result.
Table 4: Comparison with Event-RGB hybrid method on the synthesized HDM-HDR-2014 dataset.
Metric
HDRevvevvnt only)
HDRev(RGB only)
HDRev
Ours
PSNR-44
13.40
14.35
22.47
30.86
SSIM-j+ f
0.545
0.546
0.777
0.853
The results on the synthesized Kalantari 13 dataset are shown in Tab. 5 The proposed method achieves the best result for PSNR-p metric and HDRev achieves the best result for SSIM- metric.
Table 5: Comparison with Event-RGB hybrid method on the synthesized Kalantari 13 dataset.
Metric
HDRev(event only)
HDRev(RGB only)
HDRev
Ours
PSNR- y t
15.08
12.63
28.05
33.65
SSIM- 1 0.773
0.684
0.972
0.943
A.2.5 Analysis of Noise Influence
In our previous experimental design, we modelled the Poisson shot noise as follows: We multiplied each pixel's normalised intensity value (ranging from 0 to 1) by 60,000 to obtain the average photon count per pixel during each readout interval. Then, we used the cumppyrandom.poiss simulate the randomness of photon arrivals according to Poisson statistics.
function to
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We also modelled the quantization noise: the residual photons at the readout moment were preserved, and the time accumulate certain number of spikes was rounded down to the nearest integer.
Furthermore, we simulated the dark current noise. We assumed that the number of electrons induced by dark current follows Gaussian distribution with mean of 400 and standard deviation of 50. If the normalised intensity at time is I, the total number of generated electrons is computed as:
0.7 x P P660000 x I) + M( 400, = 50)
(12)
where 0.7 represents the photoelectric conversion rate. The results HDM-HDR-200 dataset are shown Tab. d
Table 6: Noise influence on the synthesized
dataset.
Metric
Spk2ImgNet. TE
BSF TE
Ours
PSNR-4 28.43
28.57
28.63
SSIM-U
0.810
0.798
0.810
The results on Kalantari dataset are shown in Tab. 7
Table 7: Noise influence on the synthesized Kalantari dataset.
Metric
Sek2ImgNet TE
BSF
Ours
PSNR-H
29.78
31.95
33.36
SSIM-
0.929
0,931
0.936
Additionally, we referred to (62). which recorded the actual dark current behaviour of spike camera. The results showed that the time intervals between dark current-inde current-induced spikes roughly follow Gaussian distribution with mean of 140 and standard deviation of 50. Based on this observation,
the total number of generated electrons is computed as:
60000
0.7 x P(60000 7) N(u = 140, a = 50)
(13)
To ensure a reasonable electron count caused by dark current, we clip the N(w 140. (0, 15000)
60000
to the range
Thh The results on HIDM-HDR-2012 dataset shown in Tab. 8
Table Noise influence on the synthesized HDM- 2014 dataset.
Metric
Spk2ImgNet TE
BSF TE
Ours
PSNR-a 25.73
25.81
25.82
SSIM-
0.725
0.751
0.752
The results on Kalantari dataset are shown in Tab.
The introduction of dark current noise inevitably degrades the reconstruction quality to some extent. Nevertheless, our reconstruction method still achieves the best performance, particularly on Kalan- taril3 dataset. We sincerely appreciate your comment, which has helped make our system design more comprehensive and reasonable.
A.2.6 Complexity Analysis
We calculate the parameter size, FLOPs, and testing time for five deep-learning- baased methods: Spk2ImgNet TE, BSF TE, MambaSpike TE and Ours. All tests are conducted on an Ubuntu 20.04 system with an Intel Core CPU and an RTX 3090 GPU. The testing time is measured by the time required to infer single image using each method. The results are shown in Tab. T0 Although our method has relatively large parameter size and FLOOPs, achieves high processing speed due its efficient high-speed parallel design.
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Table 9: Noise influence on the synthesized Kalantari 3 dataset.
Metrie
Spk2ImgNeL_TE
BSF_TE TE
Ours
PSNR-j 27.81
29.95
31.16
SSIM-u 0.897
0.900
0.904
Table 10: Comparisons of parameter size, FLOPs and testing time.
Method
Parameter
FLOPS
Testing Time
Spk2ImgNet_TR
3.760M
1.955T
0.418s
BSF_TF TE
2.071M
0.887T
0.231s
MambaSpike__a TE
4.418M
1.919T
0.004s
Ours
6.479M
3.711T
0.005s
A.2.7
Experimental Results
Further visualizations of the experimental results are shown in Fig. Fig.
Fig.
Fig.
and Fig.
10
TELML
Spi2llngNet
MambaSpiike
TFL_B TE
TFP.
Sp12lmnNet_ TE
BSF.TE TE
Mamkaspikk TE
Ground Truth
Figure 6: Visual comparisons of different reconstruction methods (part
18

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MLL
TFP ML
Spi2ImnNet_ ML
BSF ML
MamboSpiko
Ours
THL TE
TFP
Sp12lmmgNt TE
BSF TE
MambuSpike TE
Ground Truth
Figure 7: Visual comparisons of different reconstruction methods (part 2).
TFL ML
TEP ML
Spk2ImnNett ML
BSF ML
Samba
Ours
TE
Spi22igNNet
MambuSpike TE
Ground Truth
Figure 8: Visual comparisons of different reconstruction methods (part 3).
19

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ML
TFP ML
SpicllngNetMii ML
BSF ML
MambaSpake
Ours
TFL TE
TFP
Sp12lmgNNt TE
BSF TE
MambuSpike TE
Ground Truth
Figure 9: Visual comparisons of different reconstruction methods (part 4).
TFL ML
TFP ML
Spk2ImnNet ML
BSF ML
Mambsopikk
Ours
TE
Spk TE
MambuSpike TE
Ground Truth
Figure 10: Visual comparisons of different reconstruction methods (part 5).
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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: In Abstract, point out that we target HDR imaging for spike camera. In the Introduction part, we claim the paper's contributions.
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Reviews:
[{"review_id": "fUzyvBg7kP", "reviewer": "Reviewer_jHPf", "summary": "Spike cameras count incoming photons and emit a binary “spike’’ whenever a preset threshold is reached.  In very bright regions multiple spikes can occur inside one read-out period, but only a single bit is stored, so the dynamic range collapses.  The authors propose a time-encoding (TE) spike camera in which the sensor stops counting after every *b* spikes (an “overflow’’) and records when that overflow occurred by sampling a high-frequency periodic timing signal.  Storing the cycle index instead of every spike both increases dynamic range and reduces energy caused by rapid firing .\n\nFor reconstruction, they introduce a learning-based pipeline that (i) extracts multi-scale gradient features from the spike stream, (ii) aligns temporally adjacent streams with a similarity-based pyramid alignment (SPA) module, and (iii) fuses spatial features via a light-intensity refinement (LIR) module.\n\nOn two synthetic HDR benchmarks (HDM-HDR-2014 and Kalantari13) the TE camera plus their network improves PSNR by 3dB and SSIM by ≈0.05 over the same architecture fed with multi-level (ML) spike data, and beats the prior MambaSpike baseline by 2–6 dB .  Ablations show each module contributes to the gain.", "questions": "Suggestions\n\n1. Provide at least SPICE or Verilog-A level simulations of pixel, CCC, and timing signal to quantify area/energy overhead versus ML spike counter.\n2. Include newer event/frame fusion HDR networks as baselines; retrain them on the same datasets to ensure fairness.\n3. Release training scripts and checkpoints at submission time and add confidence intervals (3 seeds) for key metrics.\n4. Analyse sensitivity to clock frequency choice f and overflow depth b; a plot of dynamic-range gain vs. energy would guide sensor designers.\n5. Test reconstruction on real spike-camera HDR sequences (e.g., hybrid SpikeCam + LED-flash dataset) to demonstrate domain robustness.", "limitations": "see above in weaknesses", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. Recording the moment of overflow is a clever way to encode high luminance while keeping digital bandwidth constant.\n2. Use of gradient-aware features and pyramid alignment match the temporal sparsity of spike data.\n3. Shows consistent improvement over both hardware (ML) and algorithmic baselines on two datasets .\n\nWeaknesses and Questions\n\n1. Lack of real-sensor evidence – all experiments are synthetic. No prototype TE chip or mixed-signal simulation is shown, so claimed energy savings remain hypothetical.\n2. Limited baseline set – comparison omits recent hybrid-sensor HDR methods that fuse events with frames or ML-based spike counters; fairness of gains is unclear.\n3. Reproducibility gaps – authors will release code only “after publication’’ and provide no error bars. Statistical significance is therefore unknown .\n4. Hardware feasibility – storing a high-frequency CCC per pixel may raise SRAM and routing pressure; silicon overhead is not analysed.\n5. Generalisation to real HDR noise – Poisson shot noise and hot pixels are not modelled in the synthetic data. Hence robustness might degrade in practice.", "was_revised": true, "final_justification": "Ive raised my score to a 4. based on the what I perceive is the addressing of concerns and to achieve consensus. Thanks all.", "rebuttal": "Thank you for your summary of our contributions and strengths, and for providing five specific weaknesses together with constructive suggestions.\n\n> 1. Lack of real-sensor evidence – all experiments are synthetic. No prototype TE chip or mixed-signal simulation is shown, so claimed energy savings remain hypothetical. (Provide at least SPICE or Verilog-A level simulations of pixel, CCC, and timing signal to quantify area/energy overhead versus ML spike counter.)\n\nThank you for your helpful comments.  We simulate the proposed method at the software level. The simulation results show that the imaging results are good under this mechanism. The bandwidth can be determined by the bit depth of the SFC and the CCC. Assuming that the bit depth of the SFC and CCC is $B$, since TE spike camera outputs either the SFC or CCC data, the number of bits read out each time is $B+1$ (overflow flag). At present, we cannot effectively simulate the energy cost.\n\n>2. Limited baseline set – comparison omits recent hybrid-sensor HDR methods that fuse events with frames or ML-based spike counters; fairness of gains is unclear. Include newer event/frame fusion HDR networks as baselines; retrain them on the same datasets to ensure fairness.\n\nThank you for your insightful comments. We have included a comparison with one of the SOTA Event-RGB hybrid methods, HDRev[1]. This is the best model we have found so far among the available open-source implementations. We retrained it on our own training dataset. Since HDRev generates colour images, we converted them to grayscale for a fair comparison. The results on the HDM-HDR-2014 dataset are shown in the table below. The proposed method achieves the best result.\n| Metric      | HDRev(event only)）|HDRev(RGB only) | HDRev | Ours   |\n|:------------:|:-------------:|:-------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$   |13.40 | 14.35        | 22.47  | **30.86**  |\n| SSIM-$\\mu \\uparrow$    |0.545| 0.546        | 0.777  | **0.853**  |\n\n\nThe results on the Kalantari13 dataset are shown in the table below. The proposed method achieves the best result for the PSNR-$\\mu$ metric, and HDRev achieves the best result for the SSIM-$\\mu$ metric.\n| Metric      | HDRev(event only)）|HDRev(RGB only) | HDRev | Ours   |\n|:------------:|:-------------:|:-------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$   |15.08 | 12.63        | 28.05  | **33.65**  |\n| SSIM-$\\mu \\uparrow$    |0.773| 0.684        | **0.972**  | 0.943  |\n\n[1]. Yang, Yixin, et al. \"Learning event guided high dynamic range video reconstruction.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n>3. Reproducibility gaps – authors will release code only “after publication’’ and provide no error bars. Statistical significance is therefore unknown. (Release training scripts and checkpoints at submission time and add confidence intervals (3 seeds) for key metrics.)\n\nThank you for your valuable comments. \n- Due to the limitations of the rebuttal process, we are unable to release our code at this stage. However, we will pay attention to this point in future submissions. Thank you for your reminder.\n\n- Regarding the error bars, we used three different random seeds to generate our training datasets (note that different seeds affect the shot noise) and trained the proposed method, Spk2ImgNet_TE, and BSF_TE. The testing results on the HDM-HDR-2014 dataset are shown in the table below. The proposed method achieves the best result.\n\n| Metric     | seed  | Spk2ImgNet_TE | BSF_TE | Ours   |\n|:------------:|:------:|:-------------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$    | seed1 | 29.64         | 29.64  | **30.86**  |\n|           | seed2 | 29.65         | 29.55  | **30.33**  |\n|           | seed3 | 29.23         | 30.22  | **30.42**  |\n|           | average | 29.51         | 29.80  | **30.54**  |\n|           | variance | 0.04         | 0.09  | 0.05  |\n| SSIM-$\\mu \\uparrow$    | seed1 | 0.830         | 0.826  | **0.853**  |\n|           | seed2 | **0.835**         | 0.818  | 0.833  |\n|           | seed3 | 0.819         | 0.833  | **0.838**  |\n|           | average | 0.828         | 0.826  | **0.841**  |\n|           | variance | 0.000045        | 0.000038 | 0.000072  |\n\nThe testing results on the Kalantari13 dataset are shown in the table below. The proposed method achieves the best result.\n\n| Metric     | seed  | Spk2ImgNet_TE | BSF_TE | Ours   |\n|:------------:|:------:|:-------------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$    | seed1 | 30.29         | 31.74  | **33.65**  |\n|           | seed2 | 30.45        | 32.48  | **32.78**  |\n|           | seed3 | 30.04         | 32.03  | **33.79**  |\n|           | average | 30.26         | 32.08  | **33.41** |\n|           | variance | 0.03         | 0.09  | 0.20  |\n| SSIM-$\\mu \\uparrow$    | seed1 | 0.937         | 0.937  | **0.943**  |\n|           | seed2 | 0.938         | 0.939  | **0.942**  |\n|           | seed3 | 0.935         | 0.938  | **0.943**  |\n|           | average | 0.937         | 0.938  | **0.943**  |\n|           | variance | -        | - | -  |\n\n\n>4. Hardware feasibility – storing a high-frequency CCC per pixel may raise SRAM and routing pressure; silicon overhead is not analysed. (Analyse sensitivity to clock frequency choice f and overflow depth b; a plot of dynamic-range gain vs. energy would guide sensor designers.)\n\nThank you for your insightful comments. To support a larger dynamic range, the bit depth of the SFC or CCC needs to be increased, which will lead to a larger bandwidth and greater energy cost. The focus of this paper is not on hardware implementation, but mainly explores the feasibility of the solution from the perspective of the working mechanism and principle design, and verifies the feasibility of the solution at the software level. The overhead and energy consumption analysis at the circuit level will be further explored by colleagues responsible for circuit design in the future.\n\n> 5. Generalisation to real HDR noise – Poisson shot noise and hot pixels are not modelled in the synthetic data. Hence, robustness might degrade in practice. (Test reconstruction on real spike-camera HDR sequences (e.g., hybrid SpikeCam + LED-flash dataset) to demonstrate domain robustness.)\n\nThank you for your helpful comments. In our simulations, we modeled Poisson shot noise as follows: we scaled each image pixel’s normalized intensity (ranging from 0 to 1) by 60,000 to obtain the average photon count per readout interval for the corresponding spike camera pixel, and then applied the function **numpy.random.poisson** to simulate the Poisson shot noise in the photon arrival process. Therefore, the proposed method exhibits a certain degree of robustness to Poisson shot noise. Currently, the proposed method can only simulate spike streams based on real datasets for conducting experiments."}, {"review_id": "v3Zbon3G5r", "reviewer": "Reviewer_YUQi", "summary": "Spike cameras offer high readout frequency but imaging HDR scenes using spike cameras remains an open problem. Prior work uses multi-level (ML) spike cameras to denote multiple spikes fired within the time frame of a single readout to increase dynamic range, while the authors claim that counting a large number of spikes can be costly in ultra-HDR scenes. To address this, the authors propose a time-encoding (TE) which records the time it takes for the spike count in every pixel to reach the maximum, thereby further increasing the dynamic range. They also propose a reconstruction method for the proposed TE spike data. Experimental results revealed that reconstructed TE spike data provides superior quality to reconstructed ML spike data.", "questions": "Can the authors comment on the operating regimes of the types of spike cameras, i.e., what dynamic range is considered HDR where ML spike cameras are applicable and what is considered ultra-HDR where TE spike cameras are needed?\n\nWhat is the bit depth and the dynamic range of images in the dataset used? How does it align with the HDR and ultra-HDR arguments?\n\nIn the experiment, ML spike cameras use an 8-bit SFC while TE spike cameras use a 5-bit SFC plus a 4-bit CCC. Is this setting fair?", "limitations": "The limitation is only briefly mentioned in a single sentence.", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe problem of interest, imaging HDR scenes with spike cameras, is a relatively new topic and worths investigating. The proposed TE spike camera is well motivated and the design is reasonable. According to the experiments, when the same reconstruction method is applied, the proposed TE spike camera consistently offers higher image quality than the existing ML spike camera. The proposed reconstruction method outperforms the other reconstruction methods investigated.\n\nWeaknesses:\nIt is not entirely clear to me how to understand TE spike cameras with reference to ML spike cameras. Do TE cameras provide higher dynamic ranges at the cost of additional resources, or do they present a better use of the circuitry and balance of bit depths? To image the same scene with a fixed dynamic range using the two types of cameras, how can we compare their energy consumption, bandwidth, latency, reconstruction difficulty etc.? In addition, the operating regimes of the two types of spike cameras in terms of dynamic range or bit depth need to be clarified.\n\nThe reconstruction section is somewhat disconnected from the proposal of TE spike cameras. It would be better if the authors could offer more insights into the rationale behind the method design.\n\nAccording to Fig 4, the visible performance gain is mainly on motion mitigation instead of dynamic range recovery. This does not align with the focus of this paper as introduced in the previous sections. Furthermore, whether the test data aligns with the ultra-HDR theme is not explained. While the TE results present higher scores and fewer blur effects, the advantages of TE spike cameras over ML ones are not fully verified by the results.\n\nThis core contribution of this work is the proposal of a new design of spike cameras, but no prototype camera or real-world experiment is presented to verify the effectiveness and practicality of the proposed device.", "was_revised": true, "final_justification": "The rebuttal has not adequately address my raised concerns, so I am keeping my rating.", "rebuttal": "Thank you for your summary of our contributions and strengths. Your comments are helpful to our paper, and my answers are as follows.\n\n**Answers to the weaknesses**\n>1. It is not entirely clear to me how to understand TE spike cameras with reference to ML spike cameras. Do TE cameras provide higher dynamic ranges at the cost of additional resources, or do they present a better use of the circuitry and balance of bit depths? To image the same scene with a fixed dynamic range using the two types of cameras, how can we compare their energy consumption, bandwidth, latency, reconstruction difficulty etc.? In addition, the operating regimes of the two types of spike cameras in terms of dynamic range or bit depth need to be clarified.\n\nThank you for your helpful comments. \n- Spike camera encodes light intensity by accumulating photons and releasing a spike when a predefined threshold is reached. However, since only one spike can be latched during each readout interval and subsequently read out, spike camera can only indicate whether the accumulated photon count has reached the threshold by the readout time. It cannot distinguish whether the threshold has been reached multiple times within the same interval.\n- Multi-Level (ML) spike camera addresses this limitation by incorporating a counter to record the number of spikes, enabling it to represent higher light intensities and thereby extend the dynamic range of spike-based imaging.\n- Building upon this, Time-Encoding (TE) spike camera introduces temporal information by recording the time required for the accumulated spike count to reach a certain value. By selectively reading either the spike count or the time information, TE spike camera achieves a further enhancement in dynamic range while maintaining a bandwidth comparable to that of ML spike camera. Of course, TE spike camera comes at the cost of additional timing circuitry.\n- The dynamic range of ML spike camera is closely related to the bit depths of both the SFC (Spiking Firing Counter). Assuming the bit depth of the SFC is 8 and that no significant motion blur occurs within the duration of 10 spike intervals in high-speed scenes, ML spike camera can theoretically achieve a dynamic range of up to 68 dB ($20\\log_{10}^{((2^8-1)*10)}$).\n- The dynamic range of TE spike camera is closely related to the bit depths of both the SFC (Spiking Firing Counter) and the CCC (Clock Cycle Counter). Assuming both the SFC and CCC are configured with an 8-bit depth, and that no significant motion blur occurs within the duration of 10 spike intervals in high-speed scenes, TE spike camera can theoretically achieve a dynamic range of up to 116 dB ($20\\log_{10}^{(2^8\\times (2^8-1)*10)}$).\n\n>2. The reconstruction section is somewhat disconnected from the proposal of TE spike cameras. It would be better if the authors could offer more insights into the rationale behind the method design.\n\nThank you for your insightful comments. A basic reconstruction result can be obtained directly based on the following equation:\n\n$$\n    I =  \\dfrac{b N_{\\theta}}{n T_r}\n$$\n\nwhere $b$ denotes the number of fired spikes, $N_{\\theta}$ is the threshold, and $nT_r$ represents the spike interval . This is also the principle of the TFI_TE method used for comparison in our experiments.\n\nTo achieve higher-quality reconstruction, we leverage deep learning techniques to effectively exploit the spatiotemporal features embedded in the TE spike stream. Specifically, we first decode the TE spike stream to obtain the number of spikes generated by each pixel at different readout moments. Given that the spike count is proportional to light intensity, its gradient serves as a useful cue for motion alignment. Motivated by this observation, we extract multi-scale gradient features from the TE spike stream and perform temporal alignment using a similarity-based multi-level pyramid network. Finally, an intensity-based refinement module is employed to fuse spatial features and generate the final reconstruction result.\n\n>3. According to Fig. 4, the visible performance gain is mainly on motion mitigation instead of dynamic range recovery. This does not align with the focus of this paper as introduced in the previous sections. Furthermore, whether the test data aligns with the ultra-HDR theme is not explained. While the TE results present higher scores and fewer blur effects, the advantages of TE spike cameras over ML ones are not fully verified by the results.\n\nThank you for your helpful comments. \n- The reconstructed images preserve details in both bright and dark regions more effectively. Please refer to Figures 5–10 in the supplementary material for more visual comparisons.\n- The HDM-HDR-2014 dataset features a dynamic range of up to 108 dB. The  Kalantari13 dataset features a dynamic range of up to 97 dB. These two widely used public HDR datasets are typically regarded as benchmark datasets for ultra-HDR scenarios due to their extreme dynamic range. The images in the HDM-HDR-2014 dataset are organised in OpenEXR format. The images in the Kalantari13 dataset are organised in .hdr format. \n\n\n>4. This core contribution of this work is the proposal of a new design of spike cameras, but no prototype camera or real-world experiment is presented to verify the effectiveness and practicality of the proposed device.\n\nThank you for your valuable comments. Our current focus is on the methodological exploration and validation. In future work, we plan to investigate hardware implementation under this novel mechanism.\n\n**Answers to the questions**\n\n>1. Can the authors comment on the operating regimes of the types of spike cameras, i.e., what dynamic range is considered HDR where ML spike cameras are applicable and what is considered ultra-HDR where TE spike cameras are needed?\n\nThank you for your valuable questions. \n- Building upon ML spike camera, TE spike camera introduces temporal information by recording the time required for the accumulated spike count to reach a certain value. By selectively reading either the spike count or the time information, TE spike camera achieves a further enhancement in dynamic range while maintaining a bandwidth comparable to that of ML spike camera. Of course, TE spike camera comes at the cost of additional timing circuitry. Therefore, TE spike camera can be viewed as an extension of ML spike camera with an enhanced representational modality.\n- The dynamic range of ML spike camera is closely related to the bit depths of both the SFC (Spiking Firing Counter). Assuming the bit depth of the SFC is 8 and that no significant motion blur occurs within the duration of 10 spike intervals in high-speed scenes, ML spike camera can theoretically achieve a dynamic range of up to 68 dB ($20\\log_{10}^{((2^8-1)*10)}$).\n- The dynamic range of TE spike camera is closely related to the bit depths of both the SFC (Spiking Firing Counter) and the CCC (Clock Cycle Counter). Assuming both the SFC and CCC are configured with an 8-bit depth, and that no significant motion blur occurs within the duration of 10 spike intervals in high-speed scenes, TE spike camera can theoretically achieve a dynamic range of up to 116 dB ($20\\log_{10}^{(2^8\\times (2^8-1)*10)}$).\n\n>2. What is the bit depth and the dynamic range of images in the dataset used? How does it align with the HDR and ultra-HDR arguments?\n\nThank you for your insightful questions. The HDM-HDR-2014 dataset features a dynamic range of up to 108 dB. The  Kalantari13 dataset features a dynamic range of up to 97 dB. These two widely used public HDR datasets are typically regarded as benchmark datasets for ultra-HDR scenarios due to their extreme dynamic range. The images in the HDM-HDR-2014 dataset are organised in OpenEXR format. The images in the Kalantari13 dataset are organised in .hdr format. \n\n\n>3. In the experiment, ML spike cameras use an 8-bit SFC while TE spike cameras use a 5-bit SFC plus a 4-bit CCC. Is this setting fair?\n\nThank you for your helpful question. For TE spike camera, it would be more intuitive to compare a configuration using an 8-bit SFC along with an 8-bit CCC against ML spike camera using an 8-bit SFC, since TE spike camera outputs either the SFC or CCC data. This design ensures a similar bandwidth to that of ML spike camera. Our intention in this experiment setting was to reduce the bandwidth consumption of TE spike camera. We apologise for any confusion this may have caused."}, {"review_id": "cIUbCdvWo6", "reviewer": "Reviewer_n2tC", "summary": "The paper introduces time-encoding (TE) spike cameras, which address limitations of existing multi-level (ML) spike cameras. Rather than storing the number of spikes that are fired during each readout interval, TE spike cameras record the time required until a set number of spikes have fired. This is beneficial for scenes with high intensity content, where high bit-depth and power are required to count the large number of spikes, because TE spike cameras can encode HDR content with less bit-depth than their ML counterpart. The authors use a neural network to extract features, perform frame alignment, and refine the reconstruction result.", "questions": "1. I would like to see more concrete examples given in your discussion of \"fast ultra-HDR\" scenes to highlight the importance and impact of TE spiking cameras. From the current discussion, it's not immediately apparent which applications this method would be most beneficial for.\n2. Are there any challenges from a practicality standpoint in building the proposed TE spiking camera? Are there any tradeoffs (or increase in complexity) for building a TE vs. ML spiking camera?\n3. Representing spike streams as a large volume $\\mathbb{R}^{H \\times W \\times T}$ is inefficient since (presumably) much of the data in the stream will be zeroes (i.e., no spikes triggered). How does this affect network training / inference at scale? Can you run directly on spike data to save memory / computation?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The paper proposes a novel temporal encoding method that offers clear benefits over conventional ML spiking approaches.\n2. Key claims and network architecture choices are empirically supported. Simulated experiments provide evidence that TE data encodes more information than ML data. The ablation study provides information about the importance of each network component.\n\nWeaknesses\n1. The experimental results are performed in simulation only.", "was_revised": true, "final_justification": "I appreciate the author's answers to my questions and also their responses + additional results given to other reviewers. I will maintain my current rating, as I find the proposed approach and simulation experiments sufficient for acceptance.", "rebuttal": "Thank you for your summary of our contributions and strengths. Your comments are helpful to our paper, and my answers are as follows.\n\n**Answers to the weaknesses**\n\n>1. The experimental results are performed in simulation only.\n\nThank you for your insightful comments. Our current focus is on the methodological exploration and validation. In future work, we plan to investigate hardware implementation under this novel mechanism.\n\n**Answers to the questions**\n\n>1. I would like to see more concrete examples given in your discussion of \"fast ultra-HDR\" scenes to highlight the importance and impact of TE spiking cameras. From the current discussion, it's not immediately apparent which applications this method would be most beneficial for.\n\nThank you for your helpful comments. In certain autonomous driving scenarios, high-speed and high-dynamic-range (HDR) conditions may occur—for example, when a vehicle enters or exits tunnels or underpasses, resulting in drastic changes in illumination. In nighttime environments, most regions may be dark, while others, such as those near streetlights, can be extremely bright. Similarly, extreme weather conditions like heavy rainfall can also create challenging HDR scenes. In future work, we plan to collect more datasets to further explore and investigate these scenarios.\n>2. Are there any challenges from a practicality standpoint in building the proposed TE spiking camera? Are there any tradeoffs (or increase in complexity) for building a TE vs. ML spiking camera?\n\nThank you for your insightful comments. In practical fabrication, the integration of both the SFC and CCC modules increases the complexity of the manufacturing process. Moreover, the timing errors introduced by CCC can significantly affect the performance of TE spike camera. Compared with ML spike camera, TE spike camera achieves an extended dynamic range at the cost of recording time information.\n>3. Representing spike streams as a large volume $\\mathbb{R}^{H\\times W\\times T}$is inefficient since (presumably) much of the data in the stream will be zeroes (i.e., no spikes triggered). How does this affect network training / inference at scale? Can you run directly on spike data to save memory / computation?\n\nThank you for your helpful question. The spike stream characterises both the number of spikes and their firing times. Therefore, it is feasible to reconstruct images by focusing only on the fired spikes (i.e., excluding the zeros) along with their corresponding timestamps. This practice can save memory/computation, especially when much of the data in the stream is zero. This practice would require modifications to the network architecture and possibly an additional mapping between spike data and their firing time. This is an insightful question, and we plan to explore it in our future work."}, {"review_id": "ER0t3XC40O", "reviewer": "Reviewer_ndvD", "summary": "To solve dynamic range limitations of conventional spike cameras, and the high energy consumption of ML spike cameras, the authors propose a time-encoding based spike camera. This camera transforms counts into a time when an overflow is reached. The authors then propose a reconstruction method for recovering traditional imagery from the TE spikes. The method is compared against ML spike cameras and various alternative reconstruction algorithms.", "questions": "1. Over what time interval are spikes passed to the reconstruction network? And how fast does the network recover full frame HDR images?\n2. What is the motivation or advantage of using a spike camera for HDR imagery over the alternatives? For instance, SPADs and event cameras are equally as fast. Why is recovering a full frame necessary for applications such as autonomous driving where the application is something else like obstacle avoidance?\n3. What is the final dynamic range of the proposed system?\n4. What is the average bandwidth / energy requirement of the proposed system versus the ML spike camera system?", "limitations": "No method limitations are discussed.\n- Some spike camera crops look blurry in comparison to the GT image.\n- Only grayscale imagery is recovered, but for consumer applications where recovering a full frame is necessary, color is often important.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n\n-\tThe paper thoroughly describes how a novel time-encoded spike cameras might operate.\n-\tThe comparison against a ML spike camera is complete and fair by examining on two datasets with many different software reconstruction algorithms. \n-\tThe ablation study shows why each part of the proposed reconstruction algorithm is useful.\n\nWeaknesses\n\n-\tComparison is limited to spike camera technology. Also, advantages or motivation of using a TE spike camera instead of any of the other methods described in the related work is missing.\n-\tEvaluation is limited to simulation under idealized settings where the only noise is due to intrinsic shot noise and not any other hardware inconsistencies.\n-\tMany figure & table captions are short making them more difficult to understand and what the reader should take away. For instance, figure 3 is confusing because an arrow from the first box flows into (c) suggesting this is one large flow diagram, but that is not the case.", "was_revised": true, "final_justification": "The authors answered all my concerns. Although the paper is limited to simulated results, the additional experiments in the rebuttal demonstrate some real world promise. The proposed method is comparable to existing methods, but is an exciting direction for spike camera technology to potentially iterate on. Adding the event camera comparison, discussion of alternative camera technology and the noise experiments to the camera ready submission will greatly enhance this work.", "rebuttal": "Thank you for your helpful comments, summary of our paper and affirmation of the performance. The questions and my answers are as follows.\n\n**Answers to the weaknesses**\n\n>1. Comparison is limited to spike camera technology. Also, advantages or motivation of using a TE spike camera instead of any of the other methods described in the related work is missing.\n\nThank you for your valuable comments. \n- Conventional RGB cameras enhance dynamic range by increasing the full-well capacity of pixels and the bit depth of analogue-to-digital conversion. However, due to their reliance on synchronous exposure and relatively long exposure times, they are prone to motion blur, making them unsuitable for high-speed scenarios. \n- Event cameras, on the other hand, record changes in light intensity with extremely high readout frequencies, enabling excellent adaptation to high-speed scenes and offering a large dynamic range. Nevertheless, they struggle to capture static objects or scenes with large-scale repetitive textures, often requiring the assistance of other kinds of cameras to reconstruct complete environmental textures. \n- Spike cameras accumulate photons and generate a spike when the threshold is reached, allowing them to capture both static and dynamic objects simultaneously. Their high readout frequency—up to 40 kHz—also makes them well-suited for high-speed imaging. However, their main limitation lies in the restricted dynamic range. In this work, we aim to improve the dynamic range of spike cameras in high-speed scenarios by modifying their working mechanism.\n- We have included a comparison with one of the SOTA Event-RGB hybrid method HDRev[1]. This is the best model we have found so far among the available open-source implementations. We retrained it on our own training dataset. Since HDRev generates colour images, we converted them to grayscale for a fair comparison. The results on the HDM-HDR-2014 dataset are shown in the table below. The proposed method achieves the best result.\n\n| Metric      | HDRev(event only)）|HDRev(RGB only) | HDRev | Ours   |\n|:------------:|:-------------:|:-------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$   |13.40 | 14.35        | 22.47  | **30.86**  |\n| SSIM-$\\mu \\uparrow$    |0.545| 0.546        | 0.777  | **0.853**  |\n\n\nThe results on the Kalantari13 dataset are shown in the table below. The proposed method achieves the best result for the PSNR-$\\mu$ metric, and HDRev achieves the best result for the SSIM-$\\mu$ metric.\n| Metric      | HDRev(event only)）|HDRev(RGB only) | HDRev | Ours   |\n|:------------:|:-------------:|:-------:|:-------:|:-------:|\n| PSNR-$\\mu \\uparrow$   |15.08 | 12.63        | 28.05  | **33.65**  |\n| SSIM-$\\mu \\uparrow$    |0.773| 0.684        | **0.972**  | 0.943  |\n\n[1]. Yang, Yixin, et al. \"Learning event guided high dynamic range video reconstruction.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.\n\n>2. Evaluation is limited to simulation under idealized settings where the only noise is due to intrinsic shot noise and not any other hardware inconsistencies.\n\nThank you for your insightful comments. Our current focus is on the methodological exploration and validation. In future work, we plan to investigate hardware implementation under this novel mechanism.\n\n> 3. Many figure & table captions are short making them more difficult to understand and what the reader should take away. For instance, figure 3 is confusing because an arrow from the first box flows into (c) suggesting this is one large flow diagram, but that is not the case.\n\nThank you for your helpful comments. We sincerely apologise for any confusion or misunderstanding that may have been caused. The arrow from the first box to $(c)$ indicates that the spike stream also serves as input to the Light Intensity-based Refinement module. We will revise both the layout of figures and their captions for improved clarity.\n\n**Answers to the questions**\n>1. Over what time interval are spikes passed to the reconstruction network? And how fast does the network recover full frame HDR images?\n\nThank you for your valuable questions. We divide the continuous 61-frame spike stream into five overlapping segments: (1–21), (11–31), (21–41), (31–51), and (41–61), which are fed into the network. The proposed method infers a $512 \\times 384$ image in 0.005 seconds.\n\n>2. What is the motivation or advantage of using a spike camera for HDR imagery over the alternatives? For instance, SPADs and event cameras are equally as fast. Why is recovering a full frame necessary for applications such as autonomous driving where the application is something else like obstacle avoidance?\n\nThank you for your insightful questions. \n- SPAD sensors are capable of photon counting and are typically designed for extremely low-light environments. However, they struggle to get the accurate photon count when the light intensity is high.\n- Event cameras, on the other hand, record changes in light intensity and generally offer a high dynamic range. Nevertheless, they struggle to capture static objects or scenes with large-scale repetitive textures, often requiring the assistance of other kind of cameras to reconstruct complete environmental textures.\n- Spike cameras accumulate photons and generate a spike when the threshold is reached, allowing them to capture both static and dynamic objects simultaneously. Their high readout frequency—up to 40 kHz—also makes them well-suited for high-speed imaging. However, their main limitation lies in the restricted dynamic range. In this work, we aim to improve the dynamic range of spike cameras in high-speed scenarios by modifying their working mechanism.\n- For obstacle avoidance scenarios, relying solely on event cameras may lead to failure in detecting texture-similar obstacles, such as a wall ahead. Furthermore, effective obstacle avoidance often requires the detection of lane markings to ensure compliance with traffic rules. Thus, recovering a full-intensity frame is often necessary for such tasks.\n\n>3. What is the final dynamic range of the proposed system?\n\nThank you for your valuable questions. The dynamic range of the TE spike camera is closely related to the bit depths of both the SFC (Spiking Firing Counter) and the CCC (Clock Cycle Counter). Assuming both the SFC and CCC are configured with an 8-bit depth, and that no significant motion blur occurs within the duration of 10 spike intervals in high-speed scenes, the TE spike camera can theoretically achieve a dynamic range of up to 116 dB ($20\\log_{10}^{(2^8\\times (2^8-1)*10)}$).\n\n>4. What is the average bandwidth / energy requirement of the proposed system versus the ML spike camera system?\n\nThank you for your insightful questions. TE spike camera outputs either the SFC or CCC data. When TE spike camera employs both an SFC and a CCC with a bit depth of $B$, and the ML spike camera uses an SFC with the same bit depth $B$, TE spike camera can ensure a bandwidth similar to that of ML spike camera. Compared with ML spike camera, TE spike camera achieves an extended dynamic range at the cost of recording time information.\n\n**Answers to the limitations**\n\n>1. Some spike camera crops look blurry in comparison to the GT image.\n\nThank you for your valuable comments. There remains potential for further improvement in handling challenging high-speed motion scenarios. We plan to explore this aspect more thoroughly in future work.\n\n>2. Only grayscale imagery is recovered, but for consumer applications where recovering a full frame is necessary, color is often important.\n\nThank you for your helpful comments. In theory, this mechanism is also applicable to color spike cameras. Alternatively, color information can be recovered by integrating an RGB camera. Your suggestion provides a valuable direction for further development."}]

Author rebuttal:

Meta-review: This paper introduces a novel time-encoding (TE) spike camera design to extend the dynamic range of conventional spike cameras. Instead of counting all spikes in high-light regions, the TE design records the overflow timing relative to a periodic clock signal, thereby reducing energy cost while maintaining bandwidth efficiency. The authors also propose a reconstruction framework combining similarity-based pyramid alignment and light-intensity refinement modules to recover HDR images from TE spike data. Experiments, conducted on synthetic HDR datasets, demonstrate improvements in PSNR and SSIM compared with both multi-level (ML) spike cameras and state-of-the-art baselines.

The reviews were split. Reviewer n2tC and Reviewer ndvD were positive (ratings around accept), appreciating the novelty and potential impact of the TE design despite the simulation-only validation. Reviewer jHPf was initially skeptical due to the lack of hardware validation and limited baselines but, after rebuttal and discussion, raised the score to borderline accept. Reviewer YUQi remained unconvinced, citing fairness of comparison between ML and TE configurations, questionable “ultra-HDR” framing, and the absence of prototype hardware, ultimately recommending rejection. Thus, the scores range from reject to accept, with opinions clearly divergent.

The rebuttal and discussion were active. The authors added comparisons with event-RGB fusion HDR methods, conducted multi-seed experiments, and simulated noise sources including photon shot noise, dark current, and quantization noise. These additions alleviated some concerns for Reviewers jHPf and ndvD, who acknowledged improved robustness and raised their scores. Reviewer n2tC remained supportive throughout, considering simulation sufficient for a methodological contribution. However, Reviewer YUQi maintained strong reservations, arguing that the HDR datasets used may not justify the “ultra-HDR” claim, that fairness of bit-depth allocation was not addressed, and that a hardware prototype is indispensable for a sensor design paper. The final positions thus remained split but trended more positive after rebuttal.

The AC believes that while the absence of hardware implementation is a limitation, the methodological contribution is significant and provides a new perspective on spike camera design. The simulations, ablation studies, and additional comparisons added in the rebuttal strengthen the case that this is a promising direction for future research. The AC considers this paper worth presenting to the community to foster further exploration and discussion, and therefore recommends acceptance.
Decision: Accept (poster)