Multi-path interference causes depth errors in indirect time-of-flight (ToF) cameras. In this paper, resolving multi-path interference caused by surface reflections using a multi-tap macro-pixel computational CMOS image sensor is demonstrated. The imaging area is implemented by an array of macro-pixels composed of four subpixels embodied by a four-tap lateral electric field charge modulator (LEFM). This sensor can simultaneously acquire 16 images for different temporal shutters. This method can reproduce more than 16 images based on compressive sensing with multi-frequency shutters and sub-clock shifting. In simulations, an object was placed 16 m away from the sensor, and the depth of an interference object was varied from 1 to 32 m in 1 m steps. The two reflections were separated in two stages: coarse estimation based on a compressive sensing solver and refinement by a nonlinear search to investigate the potential of our sensor. Relative standard deviation (precision) and relative mean error (accuracy) were evaluated under the influence of photon shot noise. The proposed method was verified using a prototype multi-tap macro-pixel computational CMOS image sensor in single-path and dual-path situations. In the experiment, an acrylic plate was placed 1 m or 2 m and a mirror 9.3 m from the sensor.

Monitoring tissue metabolic and perfusion information such as oxygenation and hemoglobin concentration plays a crucial role in healthcare and therapy. Traditional methods commonly used to obtain such information include using a pulse oximeter¹ attached to the patient’s finger or employing near-infrared spectroscopy (NIRS)² using probes. However, neither of them can provide noncontact measurements nor cover a wide field of view. Spatial frequency domain imaging (SFDI) is an imaging technique that projects structured light with different spatial frequencies and phases onto diffusely reflecting objects to quantify absorption and reduced scattering coefficients.^3,4 SFDI is a noninvasive, noncontact, real-time, wide-field, quantitative imaging technique. Compact SFDI systems designed for bedside use have been employed in various experiments, including predicting diabetic foot ulcer,^{5 – 7} oxygenation measurement during reconstructive breast surgery,⁸ monitoring skin cancer,⁹ imaging and staging pressure ulcers,¹⁰ among others. SFDI is a particularly promising method for diagnosing burn severity since changes in reduced scattering measured using SFDI have been shown to correlate with histologically verified changes in collagen as a function of burn severity. These changes can be detected as early as 24 h after the burn.^11,12 This change in scattering, along with tissue oxygen saturation (${\mathrm{StO}}_2$) and total hemoglobin concentration ($c_{\mathrm{tHB}}$) derived from absorption coefficients, enable clinicians to make improved treatment decisions and can sometimes lead to early recovery for the patient.¹³ To accurately quantify tissue metabolic information, particularly the concentrations of oxy/deoxy hemoglobin, we require SFDI measurements at two or more wavelengths. However, since the presence of melanin in the epidermis can complicate the measurement results of other tissue chromophores, at least three wavelengths (as many as the number of chromophores) are generally required to achieve accurate determination of oxy and deoxyhemoglobin concentrations.

Multitap CMOS image sensors offer the capability of programmable exposure patterns. This paper proposes a method to extend the dynamic range by changing the duty cycle of the exposure for each tap. Unlike the overflow method, the proposed approach does not require any large in-pixel capacity. It also allows for dynamic range adjustment during operation, similar to the multi-exposure method, while significantly reducing motion artifacts and LED flicker. The maximum dynamic range of the proposed method is determined by factors such as reset noise, read noise, and shutter efficiency of the sensor. In this work, we demonstrated the extension of the dynamic range up to 110.1 dB with a four-tap ToF CMOS image sensor.

We report a vein imaging system that combines spatial frequency domain imaging (SFDI) and near-infrared spectroscopy (NIRS) using a 4-tap CMOS image sensor and an illumination of scanned multiple lines. Because the reflected light intensities for SFDI and NIRS are very different, high dynamic range (HDR) is necessary. Our laboratory-designed 4-tap CMOS image sensor achieves up to a dynamic range of 110.1dB by combining four images captured simultaneously. A different duty cycle or exposure time is assigned to each tap. The depth of observed tissues depends on the spatial frequency or the line pitch. SFDI uses the fast Fourier transform to obtain spatial frequency components. In NIRS, it is possible to numerically obtain images for narrower line pitches by adding two images with the opposite spatial phases. This allows vein authentication to utilize the depth information of vein. In the experiment, we built an imaging system based on a multi-line laser at a wavelength of 785nm, a galvanometer mirror, and a 4-tap 648 × 484-pixel CMOS image sensor that operates in the global shutter mode. 32HDR images were captured while scanning the multiple lines. By using the images of the tap-1 and tap-2, simultaneous acquisition of vein patterns for both SFDI and NIRS was performed.

Single-exposure high dynamic range (HDR) imaging is critical for applications such as automotive and surveillance cameras, where motion artifacts and light emitting diode (LED) flicker are significant challenges. Charge-splitting HDR imaging using multi-tap complementary metal-oxide-semiconductor (CMOS) image sensors (CIS) effectively mitigates these issues and offers programmable dynamic range extension, demonstrating significant potential for such applications. In this work, we present a model to describe the performance of the charge-splitting pixel. Then, we experimentally characterize and tune the performance of a 4-tap CIS. Through performance tuning, the image sensor achieves a single-exposure dynamic range (DR) of 126 dB. This represents an improvement of 16 dB over the previously reported 110 dB while maintaining a high signal-to-noise ratio (SNR), with a minimum transition SNR exceeding 30 dB.