Notice of Violation of IEEE Publication Principles: Tone Mapping Beyond the Classical Receptive Field

Abstract

Notice of Violation of IEEE Publication Principles<br><br> "Tone Mapping Beyond the Classical Receptive Field,"<br> by Shao-Bing Gao, Min-Jie Tan, Zhen He, and Yong-Jie Li<br> in the IEEE Transactions on Image Processing, vol. 29, February 2020, pp. 4174‐4187<br><br> After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains significant portions of content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following paper<br><br> "Colour Constancy Beyond the Classical Receptive Field,"<br> by Arash Akbarinia, and C. Alejandro Parraga,<br> in the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 9,September 2018, pp. 2081‐2094<br> <br/> Some neurons in the primary visual cortex (V1) of human visual system (HVS) conduct dynamic center-surround computation, which is thought to contribute to compress the high dynamic range (HDR) scene and preserve the details. We simulate this dynamic receptive field (RF) property of V1 neurons to solve the so-called tone mapping (TM) task in this paper. The novelties of our method are as follows. (1) Cortical processing mechanisms of HVS are modeled to build a local TM operation based on two Gaussian functions whose kernels and weights adapt according to the center-surround contrast, thus reducing halo artifacts and effectively enhancing the local details of bright and dark parts of image. (2) Our method uses an adaptive filter that follows the contrast levels of the image, which is computationally very efficient. (3) The local fusion between the center and surround responses returned by a cortical processing flow and the global signals returned by a sub-cortical processing flow according to the local contrast forms a dynamic mechanism that selectively enhances the details. Extensive experiments show that the proposed method can efficiently render the HDR scenes with good contrast, clear details, and high structural fidelity. In addition, the proposed method can also obtain promising performance when applied to enhance the low-light images. Furthermore, by modeling these biological solutions, our technique is simple and robust considering that our results were obtained using the same parameters for all the datasets (e.g., HDR images or low-light images), that is, mimicking how HVS operates.