With the advent of cellphone cameras, in particular, on smartphones, many people now take photos of themselves alone and with others in the frame; such photos are popularly known as “selfies”. Most smartphones are equipped with two cameras: the front-facing and rear cameras. The camera located on the back of the smartphone is referred to as the “out-camera,” whereas the one located on the front of the smartphone is called the “in-camera.” In-cameras are mainly used for selfies. Some smartphones feature high-resolution cameras. However, the original image quality cannot be obtained because smartphone cameras often have low-performance lenses. Super resolution (SR) is one of the recent technological advancements that has increased image resolution. We developed a new SR technology that can be processed on smartphones. Smartphones with new SR technology are currently available in the market have already registered sales. However, the effective use of new SR technology has not yet been verified. Comparing the image quality with and without SR on smartphone display is necessary to confirm the usefulness of this new technology. Methods that are based on objective and subjective assessments are required to quantitatively measure image quality. It is known that the typical object assessment value, such as Peak Signal to Noise Ratio (PSNR), does not go together with how we feel when we assess image/video. When digital broadcast started, the standard was determined using subjective assessment. Although subjective assessment usually comes at high cost because of personnel expenses for observers, the results are highly reproducible when they are conducted under right conditions and statistical analysis. In this study, the subjective assessment results for selfie images are reported.

Super resolution (SR) studies started in the 1990s and many papers were issued in the 2000s. Super resolution image reconstruction (SRR) is one of the most common SR methods. SRR reconstructs a high-resolution image (HRI) using multiple low-resolution images (LRIs). However, it requires that certain conditions are met for it to work based on the SRR algorithm. Aliasing in LRIs is one of the essential conditions for SRR. Aliasing occurs when the signal is sampled with specific frequencies not satisfying the Nyquist sampling theorem. In the beginning of this century, analog cameras were replaced with digital cameras. The digital cameras are equipped with digital imaging devices, such as complementary metal oxide semiconductors (CMOS), and charge-coupled devices (CCD), for imaging process. The imaging devices have imaging cells with finite sizes and areas. The cells generate aliasing in accordance with their fill factors. The ability of SRR is deeply related to the performance of imaging devices. In this paper, the relationship is discussed theoretically.

The medical field has long used a variety of imaging methods, including X-ray, PET, CT and MRI, for diagnoses. Video cameras inserted into the body are also used during treatment. The problem is that the resolutions of the images and video produced by these methods are almost always insufficient in some way. Enhancer is a typical technology for improving image quality. It is cost effective and has been widely used. However, Enhancer does not actually increase the resolution. It just amplifies high-frequency elements and is not effective on blurry images. If one wants to really improve the resolution of a blurry image, it is necessary to create high-frequency elements that the original image does not have. In this paper, we propose a novel nonlinear signal processing method that can create such high-frequency elements that the original image does not have. It is sufficiently simple to be installed in real-time video systems such as gastroscopic camera systems.

A method is proposed for preventing unauthorized face image revelation through unintentional capture of facial images. Methods such as covering the face and painting particular patterns on the face effectively prevent detection of facial images but hinder face-to-face communication. The proposed method overcomes this problem through the use of a device worn on the face that corrupts the Haar-like features through the use of light absorbing and reflecting materials, which makes faces in captured images mostly undetectable. The device is similar in appearance to a pair of eyeglasses, so face-to-face communication is only slightly hindered, and it does not need a power supply. Testing of a prototype "privacy visor" showed that captured facial images are sufficiently corrupted to prevent unauthorized face image revelation by face detection.

A previously proposed method for preventing the illegal recording of movies, which uses a charge-coupled device or a complementary metal oxide semiconductor device to add noise signals to the recorded content, is ineffective when the digital camera or camcorder used for the recording is equipped with an infrared-cut filter. The method has now been enhanced to make it effective even when an infrared-cut filter is used. Infrared rays specularly reflected from the filter are detected using an IR camcorder with a short-wavelength cut filter. Testing using a prototype system implemented on a 100-inch cinema screen showed that the enhanced method effectively detects in real time a camera or camcorder equipped with an infrared-cut filter.

A method is proposed for preventing privacy invasion through unintentional capture of facial images. Prevention methods such as covering the face and painting particular patterns on the face are effective but hinder face-to-face communication. The proposed method overcomes this problem through the use of a device worn on the face that transmits near-infrared signals that are picked up by camera image sensors, which makes the face in captured images undetectable. The device is similar in appearance to a pair of eyeglasses, and the signals cannot be seen by the human eye, so face-to-face communication is not hindered. Testing of a prototype "privacy visor" showed that it can effectively prevent privacy invasion via face detection by corrupting the facial images.

A new signal processing method to improve video image quality is proposed in this paper. Improving video image resolution has been researched for many years. The method of edge enhancing is usually known as Sharpness Circuit (SC), Enhancer or Unsharp Mask,. It is widely used since it works in real time and it is cost efficient. However, edge enhancing does not actually improve the degree of resolution but increases perceived resolution. It is almost impossible to create components exceeding the Nyquist frequency using conventional linear signal processing methods. Super Resolution (SR) is a highly interesting research field and many ideas and methods have been proposed, some of which have the potential to actually enhance the resolution. However, these ideas have not been widely discussed in the frequency domain. A new signal processing method that creates components exceeding the Nyquist frequency is proposed. The simulation results are discussed with regard to time domain as well as to the frequency domain.

A previously proposed method using infrared (IR) rays to prevent the re-recording of images and videos corrupts the recorded content by adding invisible noise signals using a charge-coupled device or CMOS device during recording. However, it is not effective against re-recording using a short wavelength pass filter (SWPF) to eliminate the noise. We have now implemented a countermeasure that makes it effective against re-recording using such filters. It detects IR rays reflected off the filter by using the IR specular reflection properties of the filter. Evaluation of the countermeasure implemented on a functional prototype system showed that it effectively detects re-recording using an SWPF.

A previously proposed method for preventing the illegal recording of displayed content by adding invisible noise signals to the recorded content during recording to make it unrecognizable has now been implemented in a stand-alone cabinet. Testing using the prototype stand-alone implementation demonstrated that it can effectively prevent illegal recording of displayed content by completely corrupting the recorded content. This illegal recording prevention method is applicable not only to content displayed on a screen but also to other things that should be protected against illegal recording such as confidential documents in a public office and art objects in a museum.

We propose a method for preventing illegal re-recording of images and videos with digital camcorders. Conventional digital watermarking techniques involve embedding content ID into images and videos, which helps to identify the place and time where the actual content was recorded. However, digital watermarking technology does not control the illegal re-recording of digital content with camcorders. The proposed method prevents such re-recording by corrupting the recorded content with an invisible noise signal using CCD or CMOS devices during recording. In this way, the re-recorded content will be unusable. To validate the results of this proposed method, we developed a functional prototype system for preventing illegal re-recording and implemented it on a 100-inch cinema screen.

A novel method for reducing video noise by using wavelet shrinkage in the temporal-spatial domain was devised and evaluated. In a temporal-spatial wavelet transform, static areas in a picture appear in a temporal low-frequency sub-band. In regards to this sub-band, the wavelet shrinkage can use a high threshold value in a shrinkage function and attains good noise-reduction performance. On the other hand, moving areas in a picture appear at the temporal high-frequency sub-band in a wavelet transform. In this sub-band, the wavelet shrinkage can not use a high threshold value, so a combined algorithm with a spatial-median filter is used. The noise-reduction performance of the proposed method was confirmed by a subjective evaluation, which showed that the quality of the noise-reduced images produced by the devised method is superior to that produced by traditional noise-reduction methods.

The Super Hi-Vision (SHV) developed by NHK is an ultrahigh-definition video system with 4000 scanning lines. Its video format comprises 7,680×4,320 pixels, which is 16 times the total number of pixels of HDTV (High Definition Television) and the frame rate is 60 Hz with progressive scanning. It has been designed to give viewers a strong sense of reality. In order to make the system suitable for practical use such as broadcasting services, a high-efficiency compression coding system is necessary. Therefore, we have developed several Super Hi-Vision codec systems based on MPEG-2 and AVC/H.264 video coding standards. SHV video signals are converted to sixteen HDTV signals and each video signal is processed by HDTV codec. The codec systems share some characteristics such as the frame synchronized encoding function. In this paper, details of these codec systems are described and transmission experiments using the codec are introduced.