Response to the reviewer iaWM's new comments
We appreciate the reviewer’s last-minute response. We want to note another factual error in this comment:
6. **I mean the first use in the main text, not the abstract. My suggestion is that the terminologies should be defined in the main text when they are used for the first time, even if they appear in the abstracts.**\
QBP is also defined on first use in the main text (L291).\
We acknowledge that SPAD is not defined in L25. The initial definition is on the same page. We will fix it in the revision.
Please find other responses below:
2. **The response lacks crucial implementation details ...**\
We stated that each real SPAD dataset has 100-130k frames in L208. Each data set has a single video. We can provide other required information in the revision. We are not sure what the term “comprehensive statistical profile” means in this context.
3. **The theoretical foundations of N2N, N2V, and GAP ...**\
We acknowledge that most convolutional neural networks can be applied to multidimensional data. In fact, we tested 3D N2N, N2V, and GAP with the same network architecture as shown in Table 1. However, directly applying these methods in 3D to 1-bit SPAD data produced suboptimal results. We also applied masked loss to 2D GAP (Table 1). It did not produce comparable results either. The combination of the 3D implementation, masked loss, and p-thinning-based sampling strategy, together with important architectural design choices such as group normalization, is the key to high-quality 1-bit quanta image reconstruction. The main contribution of this work is the combined concept and approach.
While QBP can be adapted for 3D data by applying a densely sampled sliding window, the original manuscript indicates that speed (30 minutes/frame) is a major limitation as noted in L295. This is not a practically viable approach for reconstructing thousands of densely sampled sliding windows. Additionally, QBP works in a fundamentally different way than our method and we are not aware of how to make fair quantitative comparisons. It registers and bins adjacent frames, therefore the output is an accumulation of aligned images instead of a direct volume prediction. Despite that, we tested real SPAD data from QBP with our method and reported the result in Fig. 5. We are not aware of any method beyond QBP for a broader comparison.
4. **To clarify, I am seeking a fair quantitative comparison ...**\
There are no differences in inference computational complexity for the 3D methods compared in Table 1. The only difference is the self-supervised training strategy. In L214, we mentioned “to ensure fair comparisons, we incorporated the same network architectures, hyperparameters, and training steps into individual baselines…” Therefore, we believe we provided a fair quantitative comparison of inference computational complexity between different methods under the same input data size and hardware requirements.
Also, this work highlights a new self-supervised concept for Bernoulli distributed data. Still, we provide a network design that is readily useful within the defined constraints. We acknowledge the network performance can be further improved using optimal architectures and hyperparameters, but it is beyond the scope of this work. The numerical information we provided in Table S1, L192, and L298 can be used to estimate the general runtime requirements in different situations by scaling the parameters.
5. **As stated in line 57, one of the paper's main contributions ...**\
We conducted extensive ablation studies besides Table S7. Table S6 and Fig. 4d compare the initial filter size and indicate both large and small sizes can degrade the performance of the network. Fig. 4a and L261 compare the group normalization size, which is key for quality reconstruction. We also consider p as a highly relevant parameter in our method, and we conducted thorough ablation studies with different p ranges in Fig. 4b, c and Table S2-5, 8. We also included results with many different experiments in hp.CSV as noted in the Table S1 caption. All of the studies include numerical results. It is unclear which specific ablation studies the reviewer wants. \
6. **See above**
7. **The statistics provided are about the simulation ...**\
We will clarify that the pixel values indicate the absolute illumination level (photon count). SPADs are very sensitive devices and work at the single photon level. Lux is a perceptual measurement of visible illuminance, which is not an appropriate measurement. For example, SPAD measures the spectrum through UV-NIR. Lux only accounts for visible light.\
The requirements/assumptions for applying this method are discussed in L49-51. In extreme cases, as shown in Fig. S4, we applied the method to a random binary array with 1% of the pixels occupied with ones. The result is an expected output of a blank image. One limitation of the method is that if the pixels are not independent, the predictions can be erroneous.