Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences

Forensically trained facial reviewers are still considered as one of the most accurate approaches for person identification from video records. The human brain can utilize information, not just from a single image, but also from a sequence of images (i.e., videos), and even in the case of low-quality records or a long distance from a camera, it can accurately identify a given person. Unfortunately, in many cases, a single still image is needed. An example of such a case is a police search that is about to be announced in newspapers. This paper introduces a face database obtained from real environment counting in 17,426 sequences of images. The dataset includes persons of various races and ages and also different environments, different lighting conditions or camera device types. This paper also introduces a new multi-frame face super-resolution method and compares this method with the state-of-the-art single-frame and multi-frame super-resolution methods. We prove that the proposed method increases the quality of face images, even in cases of low-resolution low-quality input images, and provides better results than single-frame approaches that are still considered the best in this area. Quality of face images was evaluated using several objective mathematical methods, and also subjective ones, by several volunteers. The source code and the dataset were released and the experiment is fully reproducible.

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Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

JURIKOM (Jurnal Riset Komputer) ◽

10.30865/jurikom.v7i3.2129 ◽

2020 ◽

Vol 7 (3) ◽

pp. 432

Author(s):

Windi Astuti

Keyword(s):

Image Processing ◽

Image Quality ◽

Median Filter ◽

Super Resolution ◽

Linear Interpolation ◽

Resolution Method ◽

Image Zooming ◽

Image Enlargement ◽

Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

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Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

10.1117/12.2253582 ◽

2017 ◽

Cited By ~ 4

Author(s):

Junko Ota ◽

Kensuke Umehara ◽

Naoki Ishimaru ◽

Shunsuke Ohno ◽

Kentaro Okamoto ◽

...

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Sparse Coding ◽

Super Resolution ◽

Resolution Method

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AN EFFICIENT METHOD TO FEED HIGH RESOLUTION IMAGES TO FACIAL ANALYSIS SYSTEMS

Graduate Research in Engineering and Technology ◽

10.47893/gret.2013.1032 ◽

2013 ◽

pp. 68-71

Author(s):

ROOPA R ◽

MRS. VANI.K. S ◽

MRS. NAGAVENI. V

Keyword(s):

Image Processing ◽

Face Recognition ◽

High Resolution ◽

Super Resolution ◽

Poor Quality ◽

Low Resolution ◽

Resolution Method ◽

Facial Analysis ◽

Face Images ◽

High Resolution Images

Image Processing is any form of signal processing for which the image is an input such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. In many facial analysis systems like Face Recognition face is used as an important biometric. Facial analysis systems need High Resolution images for their processing. The video obtained from inexpensive surveillance cameras are of poor quality. Processing of poor quality images leads to unexpected results. To detect face images from a video captured by inexpensive surveillance cameras, we will use AdaBoost algorithm. If we feed those detected face images having low resolution and low quality to face recognition systems they will produce some unstable and erroneous results. Because these systems have problem working with low resolution images. Hence we need a method to bridge the gap between on one hand low- resolution and low-quality images and on the other hand facial analysis systems. Our approach is to use a Reconstruction Based Super Resolution method. In Reconstruction Based Super Resolution method we will generate a face-log containing images of similar frontal faces of the highest possible quality using head pose estimation technique. Then, we use a Learning Based Super-Resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. Hence the total system quality factor will be improved by four.

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Super-Resolution Method of Closely Spaced Objects Based on Sparse Reconstruction Using Single Frame Infrared Data

Acta Optica Sinica ◽

10.3788/aos201333.0411001 ◽

2013 ◽

Vol 33 (4) ◽

pp. 0411001

Author(s):

张慧 Zhang Hui ◽

徐晖 Xu Hui ◽

林两魁 Lin Liangkui

Keyword(s):

Super Resolution ◽

Sparse Reconstruction ◽

Resolution Method ◽

Single Frame ◽

Infrared Data

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A Single Frame Super-Resolution Method Based on Matrix Completion

2012 Data Compression Conference ◽

10.1109/dcc.2012.36 ◽

2012 ◽

Cited By ~ 4

Author(s):

Fu Changjun ◽

Ji Xiangyang ◽

Zhang Yongbing ◽

Dai Qionghai

Keyword(s):

Matrix Completion ◽

Super Resolution ◽

Resolution Method ◽

Single Frame

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Multiframe Super-Resolution of Color Images Based on Cross Channel Prior

Symmetry ◽

10.3390/sym13050901 ◽

2021 ◽

Vol 13 (5) ◽

pp. 901

Author(s):

Shen Shi ◽

Bing Xiangli ◽

Zengshan Yin

Keyword(s):

Color Image ◽

Super Resolution ◽

Color Images ◽

Experimental Results ◽

Reconstruction Method ◽

Resolution Method ◽

The Cross ◽

Color Channels

Color images have a wider range of applications than gray images. There are two ways to extend the traditional super-resolution reconstruction method to color images: Super resolution reconstructs each channel of the color image individually; Change the RGB color bands into YCrCb color bands, then super-resolution reconstructs the luminance component and interpolates the chrominance components.These algorithms cannot effectively utilize the property that the edges and textures are similar in the RGB channels, and the results of those methods may lead to color artifacts. Aiming to solve these problems, we propose a new super-resolution method based on cross channel prior. First, a cross channel prior is proposed to describe the similarity of gradient in RGB channels. Then, a new super-resolution method is proposed for color images via combination of the cross channel prior and the traditional super-resolution methods. Finally, the proposed method reconstructs the color channels alternately. The experimental results show that the proposed method could effectively suppress the generation of color artifacts and improve the quality of the reconstructed images.

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FAST AND ACCURATE MULTI-FRAME SUPER-RESOLUTION OF SATELLITE IMAGES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-1-2020-57-2020 ◽

2020 ◽

Vol V-1-2020 ◽

pp. 57-64

Author(s):

J. Anger ◽

T. Ehret ◽

C. de Franchis ◽

G. Facciolo

Keyword(s):

Satellite Images ◽

Spline Interpolation ◽

Super Resolution ◽

Interpolation Model ◽

Resolution Method ◽

Still Image ◽

Small Satellites ◽

High Resolution Image ◽

Video Frames ◽

Order Spline

Abstract. Recent constellations of small satellites, such as Planet’s SkySats, offer new acquisition modes where very short videos or bursts of images are acquired instead of a single still image. Compared to sequences of multi-date images, these sequences of consecutive video frames yield a large redundancy of information within the range of seconds. This redundancy enables to increase the spatial resolution using multi-frame super-resolution algorithms. In this paper, we propose a novel super-resolution method based on a high-order spline interpolation model that combines multiple low-resolution frames to produce a high-resolution image. Moreover this method can be implemented efficiently on GPU to process entire images from real satellite acquisitions. Synthetic and real experiments show that the proposed method is able to recover fine details, and measurements of the resulting resolution indicate a gain of 10 cm / pixel with respect to Planet’s SkySat standard imagery products.

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