Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study proposes a dark-light image enhancement method based on the cooperative constraint of multiple autoencoder priors (CCMP), addressing issues of overexposure and underexposure in handling complex images observed in previous algorithms. The approach comprises two prior modules and an enhancement module, utilizing autoencoders to learn the feature distribution of dark-light images under normal exposure as a prior term. The enhancement process is guided by curve mapping fitting and an image difference evaluation function to restore global illumination and local details, constructing a more robust and detailed feature enhancement network. Extensive comparative experiments demonstrate that this method improves detail contrast, reduces artifacts and noise, yielding superior enhancement results across various scenarios. This contributes significantly to the field of dark-light image enhancement. Although the author's research has certain innovation and significance, there are still some issues that need further improvement. As follows,

(1) The unknown variables mentioned in equation (15) need to be defined.

(2) Although the Dark light image enhancement method proposed by the author shows promising results in terms of image quality metrics such as SSIM and PSNR, it is also important to consider the model inference time and model size, which are crucial factors in practical applications.

(3) The PSNR in Table 1 and Table 2 has no unit.

(4) The introduction of innovations is not clear enough. There is less introduction to the proposed method, which can explain in more detail the unique features and improvements of the method proposed in the paper compared with existing methods.

Comments on the Quality of English Language

Moderate editing of English language required

Author Response

Dear reviewers, our replies to your comments and suggestions are written in the document, please check the attachments.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper proposes a method for dark light image enhancement based on deep neural networks (U-net autoencoder) and multiple self-encoding prior collaborative constraints. Hereafter my comments:

- Mathematical notations and equations, and their styles, should be improved for a scientific research article.

- In Section 3.3.3 it seems to me that a paragraph is missing: how image integrity loss is defined? Is the composition of the losses in Eq. (15)? l_EN is defined somewhere? Please also add a brief sentence at the beginning of the Section 3.3.3, just to introduce the definition.

- In the experimental Section 4, authors introduce image quality metrics. I recommend providing formal definition of the metrics for a thorough work.

 

Author Response

Dear reviewers, our replies to your comments and suggestions are written in the document, please check the attachments.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Authors addressed all the reviewers' comments. The paper recommend it for the publication.