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Gaze estimation suffers from burdensome personal calibration or complex all-device calibration. Self-calibrating methods can meet this challenge but depend on scenes and sacrifice accuracy. We propose a flexible and accurate gaze estimation approach calibrated implicitly with potential gaze patterns. By constructing an optical axis projection (OAP) plane and a visual axis projection (VAP) plane simultaneously, the optical axis and the visual axis can be represented as 2-D points, i.e., the OAP and VAP, which have a similarity transformation, indicating the linear consistency of OAP patterns with gaze patterns. Hence, a 3-D gaze estimation model using the OAP as an eye feature to predict the VAP is built. The unknown parameters are calculated separately by linearly aligning OAP patterns to natural and easily detectable gaze patterns. Experimental results show that the proposed gaze estimation approach is more accurate than state-of-the-art head-mounted gaze estimation methods, which require explicit calibration or multiscene saliency.

Brightness order reversal could happen among shadow regions in a bright image and high-light regions in a dark image if two large-exposure-ratio images are fused directly by using existing multi-scale exposure fusion (MEF) algorithms. This problem can be addressed effectively via exposure interpolation. In this paper, a novel exposure interpolation algorithm is introduced by combining model-based and data-driven approaches to form a neural augmented interpolation framework. An image with a medium-exposure is initially interpolated by using intensity mapping functions (IMFs), and then refined via a novel exposedness aware network (EA-Net). Experimental results indicate that the model-based approach is improved by the data-driven approach, and the data-driven approach is benefited from the model-based method for fast convergence speed and learning with few training samples. The explainability of both the new EA-Net and the proposed framework is improved via such a neural augmentation.

There are shadow and highlight regions in a low-dynamic-range (LDR) image which is captured from a high-dynamic-range (HDR) scene. It is an ill-posed problem to restore the saturated regions of the LDR image. In this article, the saturated regions of the LDR image are restored by fusing model-based and data-driven approaches. With such a neural augmentation, two synthetic LDR images are first generated from the underlying LDR image via the new model-based approach. It relaxes the requirement of mapping integers to integers and improves the modeling accuracy. One is brighter than the input image to restore the shadow regions and the other is darker than the input image to restore the highlight regions. Both synthetic images are then refined via one single exposedness-aware saturation restoration network (EASRN). Finally, the two synthetic images and the input image are combined together via an HDR synthesis algorithm or a multiscale exposure fusion (MEF) algorithm. Experimental results indicate that the proposed algorithm outperforms existing algorithms in terms of HDR-VDP-3. The proposed algorithm can be embedded in any smartphones or digital cameras to produce an information-enriched LDR image.

In phase-measurement profilometry (PMP), the gamma effect can cause severe nonlinear distortion of the phase pattern (i.e., water ripples on the surface profile). Gamma correction is an effective method to eliminate the gamma effects of commercial projectors. However, projector defocusing on the suppression of higher harmonics inevitably results in an estimated gamma deviation from the true value. In this study, gamma mapping is constructed using the duty ratio (DR) to code the chord distribution of the simulated distorted phase while considering projector defocusing. With the known gamma mapping, the accurate gamma is calculated by DR coding of the actual distorted phase under projector defocusing. Simulated experiments verified that the relative errors of the gamma calculated by the proposed method under different degrees of defocus were less than 3.5%. Furthermore, the experimental results demonstrate that the proposed gamma calculation method is robust to the defocus effect of the projector and that a smoother surface can be reconstructed after gamma correction.