Mainstream data augmentation techniques involving image-level manipulation operations (e.g., CutMix) compromise the integrity of extracted features, which impedes the application of data augmentation for pixel-level image processing tasks. Moreover, the unexplored potential of test-time augmentation within the HDR domain remains to be validated. In this paper, a full stage data augmentation method called HDR-VDA for HDR video reconstruction is proposed, especially for synthetic video based training datasets. In the training stage, the local area-based mixed data augmentation (LMDA) provides samples encompassing diverse exposure and color patterns, thus the trained model gains improved capabilities in effectively processing poorly-exposure regions, with particular emphasis on areas with rich color details. A motion and ill-exposure guided sample rank and adjustment strategy (MISRA) is utilized to augment specific training samples and compensate extra information. In the testing stage, an HDR-targeted test-time augmentation method (HTTA) is designed for reconstructed HDR frames. After restoring the shape of the test-time augmented HDR output to be consistent with the original inference output, an ill-exposure outlier removal based average ensemble method is used to blend all augmented inference outputs to generate reliable and stable reconstruction results. Experiments demonstrate that HDR-VDA achieves a better PSNR-T score of 38.91 dB, compared with conventional works under the same conditions.

HDR video is possible to display the maximum 1200% luminance, however, it is limited in SDR display. In this study, we expand high luminance area considering with perceptual performance to improve a presentation performance of HDR video in the SDR display. As results of objective experiments, it is recognized that the proposed method can improve the presentation performance maximally 0.8dB in WPSNR.

This paper presents a fully integrated yet compact receiver front-end for Sub-GHz applications such as Internet-of-Things (IoT). The low noise amplifier (LNA) matching network leverages an inductance boosting technique. A relatively small on-chip inductor with a compact area achieves impedance matching in such a low frequency. Moreover, a passive-mixer-first mode bypasses the LNA to extend the receiver dynamic-range. The passive mixer provides matching to the 50Ω antenna interface to eliminate the need for additional passive components. Therefore, the receiver can be fully-integrated without any off-chip matching components. The flipped-voltage-follower (FVF) cell is adopted in the low pass filter (LPF) and the variable gain amplifier (VGA) for its high linearity and low power consumption. Fabricated in 65nm LP CMOS process, the proposed receiver front-end occupies 0.37mm2 core area, with a tolerable input power ranging from -91.5dBm to -1dBm for 500kbps GMSK signal at 924MHz frequency. The power consumption is 1mW power under a 1.2V supply.

In this paper, we propose an image adjustment method for multi-exposure images based on convolutional neural networks (CNNs). We call image regions without information due to saturation and object moving in multi-exposure images lacking areas in this paper. Lacking areas cause the ghosting artifact in fused images from sets of multi-exposure images by conventional fusion methods, which tackle the artifact. To avoid this problem, the proposed method estimates the information of lacking areas via adaptive inpainting. The proposed CNN consists of three networks, warp and refinement, detection, and inpainting networks. The second and third networks detect lacking areas and estimate their pixel values, respectively. In the experiments, it is observed that a simple fusion method with the proposed method outperforms state-of-the-art fusion methods in the peak signal-to-noise ratio. Moreover, the proposed method is applied for various fusion methods as pre-processing, and results show obviously reducing artifacts.

We propose a HDR (high dynamic range) reconstruction method in an image sensor with a pixel-parallel ADC (analog-to-digital converter) for non-destructively reading out the intermediate exposure image. We report the circuit design for such an image sensor and the evaluation of the basic HDR reconstruction method.

A method for detecting the timing of photodiode (PD) saturation without using an in-pixel time-to-digital converter (TDC) is proposed. Detecting PD saturation time is an approach to extend the dynamic range of a CMOS image sensor (CIS) without multiple exposures. In addition to accumulated charges in a PD, PD saturation time can be used as a signal related to light intensity. However, in previously reported CISs with detecting PD saturation time, an in-pixel TDC is used to detect and store PD saturation time. That makes the resolution of a CIS lower because an in-pixel TDC requires a large area. As for the proposed pixel circuit, PD saturation time is detected and stored as a voltage in a capacitor. The voltage is read and converted to a digital code by a column ADC after an exposure. As a result, an in-pixel TDC is not required. A signal-processing and calibration method for combining two signals, which are saturation time and accumulated charges, linearly are also proposed. Circuit simulations confirmed that the proposed method extends the dynamic range by 36 dB and its total dynamic range to 95 dB. Effectiveness of the calibration was also confirmed through circuit simulations.

At present, the generative adversarial network (GAN) plays an important role in learning tasks. The basic idea of a GAN is to train the discriminator and generator simultaneously. A GAN-based inverse tone mapping method can generate high dynamic range (HDR) images corresponding to a scene according to multiple image sequences of a scene with different exposures. However, subsequent tone mapping algorithm processing is needed to display it on a general device. This paper proposes an end-to-end multi-exposure image fusion algorithm based on a relative GAN (called RaGAN-EF), which can fuse multiple image sequences with different exposures directly to generate a high-quality image that can be displayed on a general device without further processing. The RaGAN is used to design the loss function, which can retain more details in the source images. In addition, the number of input image sequences of multi-exposure image fusion algorithms is often uncertain, which limits the application of many existing GANs. This paper proposes a convolutional layer with weights shared between channels, which can solve the problem of variable input length. Experimental results demonstrate that the proposed method performs better in terms of both objective evaluation and visual quality.

We propose image synthesizing using luminance adapted range compression and detail-preserved blending. Range compression is performed using the correlated visual gamma then image blending is performed by local adaptive mixing and selecting method. Simulations prove that the proposed method reproduces natural images without any increase in noise or color desaturation.

This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE enables us not only to produce images without color saturation regions from a single-shot image, but also to avoid ghost artifacts in the producing ones. However, the number of exposures is generally limited to two, and moreover it is difficult to decide the optimum exposure values before the photographing. In the proposed scheme, a scene segmentation method is applied to input multi-exposure images, and then the luminance of the input images is adjusted according to both of the number of scenes and the relationship between exposure values and pixel values. The proposed method with the luminance adjustment allows us to improve the above two issues. In this paper, we focus on dual-ISO imaging as one of single-shot imaging. In an experiment, the proposed scheme is demonstrated to be effective for single-shot high dynamic range imaging with SVE, compared with conventional MEF schemes with exposure compensation.

An HDR (High Dynamic Range) image synthesis is a method which is to photograph scenes with wide luminance range and to reproduce images close to real visual scenes on an LDR (Low Dynamic Range) display. In general, HDR images are reproduced by taking images with various camera exposures and using the tone synthesis of several images. In this paper, we propose an HDR image tone mapping method based on a visual brightness function using dual exposed images and a synthesis algorithm based on local surround. The proposed algorithm has improved boundary errors and color balance compared with existing methods. Also, it improves blurring and noise amplification due to image mixing.

This letter considers a unified tone mapping operation (TMO) for HDR images. The unified TMO can perform tone mapping for various HDR image formats with a single common operation. The integer TMO which can realize unified tone mapping by converting an input HDR image into an intermediate format is proposed. This method can be executed efficiently with low memory and low performance processor. However, only floating-point HDR image formats have been considered in the method. In other words, a long-integer which is one of the HDR image formats has not been considered in the method. This letter applies the method to a long-integer format, and confirm its performance. The experimental results show the proposed method is effective for an integer format in terms of the resources such as the computational cost and the memory cost.

We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.

A novel dynamic element matching (DEM) method, called binary-tree random DEM (BTR-DEM), is presented for a Nyquist-rate current-steering digital-to-analog converter (DAC). By increasing or decreasing the number of unit current sources randomly at the same time, the BTR-DEM encoding reduces switch transition glitches. A 5-bit current-steering DAC with the BTR-DEM technique is implemented in a 65-nm CMOS technology. The measured spurious free dynamic range (SFDR) attains 42 dB for a sample rate of 100 MHz and shows little dependence on signal frequency.

High dynamic range (HDR) images that include large differences in brightness levels are studied to address the lack of knowledge on the quality estimation method for real HDR images. For this, we earlier proposed a new metric, the independent signal-to-noise ratio (ISNR), using the independent pixel value as the signal instead of the peak value (PSNR). Next, we proposed the local peak signal-to-noise ratio (LPSNR), using the maximum value of neighboring pixels, as an improved version. However, these methods did not sufficiently consider human perception. To address this issue, here we proposed an objective estimation method that considers spatial frequency characteristics based on the actual brightness. In this method, the approximated function for human characteristics is calculated and used as a 2D filter on an FFT for spatial frequency weighting. In order to confirm the usefulness of this objective estimation method, we compared the results of the objective estimation with a subjective assessment. We used the organic EL display which has a perfect contrast ratio for the subjective assessment. The results of experiments showed that perceptual weighting improves the correlation between the SNR and MOS of the subjective assessment. It is recognized that the weighted LPSNR gives the best correlation.

A global tone mapping operation (TMO) for high dynamic range (HDR) images with fixed-point arithmetic is proposed and evaluated in this paper. A TMO generates a low dynamic range (LDR) image from an HDR image by compressing its dynamic range. Since an HDR image is generally expressed in a floating-point data format, a TMO also deals with floating-point data even though a resultant LDR image is integer data. The proposed method treats a floating-point number as two 8-bit integer numbers which correspond to an exponent part and a mantissa part, and applies tone mapping to these integer numbers separately. Moreover, the method conducts all calculations in the tone mapping with only fixed-point arithmetic. As a result, the method reduces a memory cost and a computational cost. The evaluation shows that the proposed method reduces 81.25% of memory usage. The experimental results show that the processing speed of the proposed method with fixed-point arithmetic is 23.1 times faster than the conventional method with floating-point arithmetic. Furthermore, they also show the PSNR of LDR images obtained by the proposed method are comparable to those of the conventional method, though reducing computational and memory cost.

This paper analyzes the transmission performances of visible light communication (VLC) based on unipolar orthogonal frequency division multiplexing (OFDM), which is compatible with intensity modulation and direct detection (IM/DD). Three existing unipolar OFDM schemes, namely DC biased optical OFDM (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM), and flip-OFDM are investigated and compared. While these three schemes have been analyzed for indoor optical wireless communication (OWC) subject to the limitation on the transmit optical power, they have not been carefully investigated and compared for VLC when a large transmit power is available due to the illumination requirement, and the signal dynamic range (DR) becomes the main limitation. For the analysis, DR expressions of DCO-OFDM, ACO-OFDM, and flip-OFDM signals are first derived. Then, the bit error rate (BER) expression of each unipolar OFDM scheme is derived in terms of the DR. For data rates in the range of 1-10Mbps, under the system parameters based on typical indoor environments, DCO-OFDM is observed to outperform the other two schemes. This superiority of DCO-OFDM is in contrast with previously reported results that indicate the attractiveness of ACO-OFDM and flip-OFDM over DCO-OFDM when the transmit optical power is the main limitation. Finally, light dimming is considered to identify the illumination level below which DCO-OFDM loses this superiority.

This paper presents a novel adaptable 4-moduli set {2n + k, 2n+1, 2n-1, 22n+1}. It offers diverse dynamic ranges (DRs) from 25n-2n to 25n + k-2n + k that are used to conquer the over-range issue in RNS-application hardware designs. The proposed adaptable set possesses the coarse parameter n and fine parameter k. It not only has better parallelism and larger dynamic range (DR) than the existing adaptive 3-moduli sets, but also holds more sizable and flexible than the general 4-moduli sets with single parameter. For the adaptable R-to-B conversion, this paper first derives a fast reverse converting algorithm based on Chinese Remainder Theorem (CRT) and then presents the efficient converter architecture. From the experimental results, the proposed adaptable converter achieves better hardware performance in various DRs. Based on TSMC 0.18 µm CMOS technology, the proposed converter design is implemented and its results get at least 20.93% saving of Area-Delay-Power (ADP) products on average when comparing with the latest converter works.

In this paper we propose a new tone mapping method for HDR video. Two types of gamma tone mapping are blended to preserve local contrast in the entire range of luminance. Our method achieves high quality tone mapping especially for the HDR video that has a nonlinear response to scene radiance. Additionally, we apply it to an object-aware tone mapping method for camera surveillance. This method achieves high visibility of target objects in the tone mapped HDR video. We examine the validity of our methods through simulation and comparison with conventional work.

Ambient light sensors have been used to reduce power consumption of Active Matrix Liquid Crystal Displays (AMLCD) adjusting display brightness depending on ambient illumination. Discrete sensors have been commonly used for this purpose. They make module design complex. Therefore it has been required to integrate the sensors on the display panels for solving the issue. So far, many kinds of integrated sensors have been developed using Amorphous Silicon (a-Si) technology or Low Temperature Polycrystalline Silicon (LTPS) technology. These conventional integrated sensors have two problems. One is that LTPS sensors have less dynamic range due to the less photosensitivity of LTPS photodiodes. The other is that both the LTPS and a-Si sensors are susceptible to display driving noises. In this paper, we introduce a novel integrated sensor using both LTPS and a-Si technologies, which can solve these problems. It consists of vertical a-Si Schottky photodiodes and an LTPS differential converter circuit. The a-Si photodiodes have much higher photosensitivity than LTPS ones, and this contributes to wide dynamic range and high accuracy. The LTPS differential converter circuit converts photocurrent of the photodiodes to a robust digital signal. In addition it has a function of canceling the influences of the display driving noises. With the circuit, the sensor can stably and accurately work even under the noises. The performance of the sensor introduced in this paper was measured to verify the advantages of the novel design. The measurement result showed that it worked in a wide ambient illuminance range of 5-55,000 lux with small errors of below 5%. It was also verified that it stably and accurately worked even under the display driving noise. Thus the sensor introduced in this paper achieved the wide dynamic range and noise robustness.

High Dynamic Range Imaging (HDRI) refers to a set of techniques that can represent a dynamic range of real world luminance. Hence, the HDR image can be used to measure the reflectance property of materials. In order to reproduce the original color of materials using this HDR image, characterization of HDR imaging is needed. In this study, we propose a new HDRI characterization method under a known illumination condition at the HDR level. The proposed method normalizes the HDR image by using the HDR image of a light and balances the tone using the reference of the color chart. We demonstrate that our method outperforms the previous method at the LDR level by the average color difference and BRDF rendering result. The proposed method gives a much better reproduction of the original color of a given material.