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A tactile image sensor employing a camera is capable of obtaining rich tactile information through image sequences with high spatial resolution. There have been many studies on the tactile image sensors from more than 30 years ago, and, recently, they have been applied in the field of robotics. Tactile image sensors can be classified into three typical categories according to the method of conversion from physical contact to light signals: Light conductive plate-based, marker displacement- based, and reflective membrane-based sensors. Other important elements of the sensor, such as the optical system, image sensor, and post-image analysis algorithm, have been developed. In this work, the literature is surveyed, and an overview of tactile image sensors employing a camera is provided with a focus on the sensing principle, typical design, and variation in the sensor configuration.

We present a 2D-stitched, 316MP, 120FPS, high dynamic range CMOS image sensor with 92 CML output ports operating at a cumulative date rate of 515 Gbit/s. The total die size is 9.92 cm × 8.31 cm and the chip is fabricated in a 65 nm, 4 metal BSI process with an overall power consumption of 23 W. A 4.3 µm dual-gain pixel has a high and low conversion gain full well of 6600e- and 41,000e-, respectively, with a total high gain temporal noise of 1.8e- achieving a composite dynamic range of 87 dB.

The Quanta Image Sensor (QIS) was conceived when contemplating shrinking pixel sizes and storage capacities, and the steady increase in digital processing power. In the single-bit QIS, the output of each field is a binary bit plane, where each bit represents the presence or absence of at least one photoelectron in a photodetector. A series of bit planes is generated through high-speed readout, and a kernel or “cubicle” of bits (x, y, t) is used to create a single output image pixel. The size of the cubicle can be adjusted post-acquisition to optimize image quality. The specialized sub-diffraction-limit photodetectors in the QIS are referred to as “jots” and a QIS may have a gigajot or more, read out at 1000 fps, for a data rate exceeding 1 Tb/s. Basically, we are trying to count photons as they arrive at the sensor. This paper reviews the QIS concept and its imaging characteristics. Recent progress towards realizing the QIS for commercial and scientific purposes is discussed. This includes implementation of a pump-gate jot device in a 65 nm CIS BSI process yielding read noise as low as 0.22 e− r.m.s. and conversion gain as high as 420 µV/e−, power efficient readout electronics, currently as low as 0.4 pJ/b in the same process, creating high dynamic range images from jot data, and understanding the imaging characteristics of single-bit and multi-bit QIS devices. The QIS represents a possible major paradigm shift in image capture.

We propose and demonstrate a novel motion blur-free hybrid image sensing technique. Unlike the previous hybrid image sensors, we developed a homogeneous hybrid image sensing technique including 60 fps CMOS Image Sensor (CIS) and 1440 fps pseudo Dynamic Vision Sensor (DVS) image frames without any performance degradation caused by static bad pixels. To achieve the fast readout, we implemented two one-side ADCs on two photodiodes (PDs) and the pixel output settling time can be reduced significantly by using the column switch control. The high-speed pseudo DVS frame can be obtained by differentiating fast-readout CIS frames, by which, in turn, the world's smallest pseudo DVS pixel (1.8 μm) can be achieved. In addition, we confirmed that CIS (50 Mp resolution) and DVS (0.78 Mp resolution) data obtained from the hybrid image sensor can be transmitted over the MIPI (4.5 Gb/s four-lane D-PHY) interface without signal loss. The results showed that the motion blur of a 60 fps CIS frame image can be compensated dramatically by using the proposed pseudo DVS frames together with a deblur algorithm. Finally, using the event simulation, we verified that a 1440 fps pseudo DVS frame can compensate the motion blur of the CIS image captured in the situation of jogging at a 3 m distance.

According to the Center for Disease Control and Prevention (CDC), the average human life expectancy is 78.8 years. Specifically, 3.2 million deaths are reported yearly due to heart disease, cancer, Alzheimer’s disease, diabetes, and COVID-19. Diagnosing the disease is mandatory in the current way of living to avoid unfortunate deaths and maintain average life expectancy. CMOS image sensor (CIS) became a prominent technology in assisting the monitoring and clinical diagnosis devices to treat diseases in the medical domain. To address the significance of CMOS image ‘sensors’ usage in disease diagnosis systems, this paper focuses on the CIS incorporated disease diagnosis systems related to vital organs of the human body like the heart, lungs, brain, eyes, intestines, bones, skin, blood, and bacteria cells causing diseases. This literature survey’s main objective is to evaluate the ‘systems’ capabilities and highlight the most potent ones with advantages, disadvantages, and accuracy, that are used in disease diagnosis. This systematic review used PRISMA workflow for study selection methodology, and the parameter-based evaluation is performed on disease diagnosis systems related to the human body’s organs. The corresponding CIS models used in systems are mapped organ-wise, and the data collected over the last decade are tabulated.

A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction quality. We validate the new algorithm on synthetic datasets, as well as real videos collected by one-bit single-photon avalanche diode (SPAD) cameras.

Image quality in high-resolution digital single-lens reflex (DSLR) systems is degraded by Complementary Metal-Oxide-Semiconductor (CMOS) sensor noise and optical imperfections. Sensor noise becomes pronounced under high-ISO (International Organization for Standardization) settings, while optical aberrations such as blur and chromatic fringing distort the signal. Optical and sensor-level noise are distinct and hard to separate, but prior studies suggest that improving optical fidelity can suppress or mask sensor noise. Upon this understanding, we introduce a framework that utilizes densely interpolated Point Spread Functions (PSFs) to recover high-fidelity images. The process begins by simulating Gaussian-based PSFs as pixel-wise chromatic and spatial distortions derived from real degraded images. These PSFs are then encoded into a latent space to enhance their features and used to generate refined PSFs via similarity-weighted interpolation at each target position. The interpolated PSFs are applied through Wiener filtering, followed by residual correction, to restore images with improved structural fidelity and perceptual quality. We compare our method—based on pixel-wise, physical correction, and densely interpolated PSF at pre-processing—with post-processing networks, including deformable convolutional neural networks (CNNs) that enhance image quality without modeling degradation. Evaluations on DIV2K and RealSR-V3 confirm that our strategy not only enhances structural restoration but also more effectively suppresses sensor-induced artifacts, demonstrating the benefit of explicit physical priors for perceptual fidelity.

Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.

In this study, we developed and demonstrated a millimeter-wave electric field imaging system using an electro-optic crystal and a highly sensitive polarization measurement technique using a polarization image sensor, which was fabricated using a 0.35-µm standard CMOS process. The polarization image sensor was equipped with differential amplifiers that amplified the difference between the 0° and 90° pixels. With the amplifier, the signal-to-noise ratio at low incident light levels was improved. Also, an optical modulator and a semiconductor optical amplifier were used to generate an optical local oscillator (LO) signal with a high modulation accuracy and sufficient optical intensity. By combining the amplified LO signal and a highly sensitive polarization imaging system, we successfully performed millimeter-wave electric field imaging with a spatial resolution of 30×60 µm at a rate of 1 FPS, corresponding to 2400 pixels/s.

A lateral overflow integration capacitor (LOFIC) complementary metal oxide semiconductor (CMOS) image sensor can realize high-dynamic-range (HDR) imaging with combination of a low-conversion-gain (LCG) signal for large maximum signal electrons and a high-conversion-gain (HCG) signal for electron-referred noise floor. However, LOFIC-CMOS image sensor requires a two-channel read-out chain for LCG and HCG signals whose polarities are inverted. In order to provide an area-efficient LOFIC-CMOS image sensor, a one-channel read-out chain that can process both HCG and LCG signals is presented in this paper. An up/down double-sampling circuit composed of an inverting amplifier for HCG signals and a non-inverting attenuator for LCG signals can reduce the area of the read-out chain by half compared to the conventional two-channel read-out chain. A test chip is fabricated in a 0.18 μm CMOS process with a metal–insulator–metal (MIM) capacitor, achieving a readout noise of 130 μVrms for the HCG signal and 1.19 V for the LCG input window. The performance is equivalent to 103 dB of the dynamic range with our previous LOFIC pixel in which HCG and LCG conversion gains are, respectively, 160 μV/e− and 10 μV/e−.