Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.

High-resolution images from a sequence of low-resolution observations

Abstract: This chapter discusses the problem of obtaining a high resolution (HR) image or sequences of HR images from a set of low resolution (LR) observations. This problem has also been referred to in the literature by the names of super-resolution (SR) and resolution enhancement (we will be using all terms interchangeably). These LR images are under-sampled and they are acquired by either multiple sensors imaging a single scene or by a single sensor imaging the scene over a period of time. For static scenes the LR observations are related by global subpixel shifts, while for dynamic scenes they are related by local subpixel shifts due to object motion (camera motion, such as panning and zooming can also be included in this model). In this chapter we will be using the terms image(s), image frame(s), and image sequence frame(s) interchangeably. This is a problem encountered in a plethora of applications. Images and video of higher and higher resolution are required, for example, in scientific (e.g., medical, space exploration, surveillance) and commercial (e.g., entertainment, high-definition television) applications. One of the early applications of high resolution imaging was with Landsat imagery. The orbiting satellite would go over the same area every 18 days, acquiring misregistered images. Appropriately combining these LR images produced HR images of the scene. Increasing the resolution of the imaging sensor is clearly one way to increase the resolution of the acquired images. This solution however may not be feasible due to the increased associated cost and the fact that the shot noise increases during acquisition as the pixel size becomes smaller. On the other hand, increasing the chip size to accommodate the larger number of

Unsupervised Learning with Mini Free Energy

Abstract: In this paper, we present an unsupervised learning with mini free energy for early breast cancer detection. Although an early malignant tumor must be small in size, the abnormal cells reveal themselves physiologically by emitting spontaneously thermal radiation due to the rapid cell growth, the so-called angiogenesis effect. This forms the underlying principle of Thermal Infrared (TIR) imaging in breast cancer study. Thermal breast scanning has been employed for a number of years, which however is limited to a single infrared band. In this research, we deploy two satellite-grade dual-color (at middle wavelength IR (3 − 5µm) and long wavelength IR (8−12µm)) IR imaging cameras equipped with smart subpixel automatic target detection algorithms. According to physics, the radiation of high/low temperature bodies will shift toward a shorter/longer IR wavelength band. Thus, the measured vector data x per pixel can be used to invert the matrix-vector equation x=As pixel-by-pixel independently, known as a single pixel blind sources separation (BSS). We impose the universal constraint of equilibrium physics governing the blackbody Planck radiation distribution, i.e., the minimum Helmholtz free energy, H = E − ToS. To stabilize the solution of Lagrange constrained neural network (LCNN) proposed by Szu et al., we incorporate the second order approximation of free energy, which corresponds to the second order constraint in the method of multipliers. For the subpixel target, we assume the constant ground state energy Eo can be determined by those normal neighborhood tissue, and then the excited state can be computed by means of Taylor series expansion in terms of the pixel I/O data. We propose an adaptive method to determine the neighborhood to find the free energy locally. The proposed methods enhance both the sensitivity and the accuracy of traditional breast cancer diagnosis techniques. It can be used as a first line supplement to traditional mammography to reduce the unwanted X-rays during the chemotherapy recovery. More important, the single pixel BSS method renders information on the tumor stage and tumor degree during the recovery process, which is not available using the popular independent component analysis (ICA) techniques.

An image registration algorithm based on phase correlation and the classical Lucas–Kanade technique

Abstract: Image registration is defined as an important process in image processing in order to align two or more images. A new image registration algorithm for translated and rotated pairs of 2D images is presented in order to achieve subpixel accuracy and spend a small fraction of computation time. To achieve the accurate rotation estimation, we propose a two-step method. The first step uses the Fourier Mellin Transform and phase correlation technique to get the large rotation, then the second one uses the Fourier Mellin Transform combined with an enhance Lucas–Kanade technique to estimate the accurate rotation. For the subpixel translation estimation, the proposed algorithm suggests an improved Hanning window as a preprocessing task to reduce the noise in images then achieves a subpixel registration in two steps. The first step uses the spatial domain approach which consists of locating the peak of the cross-correlation surface, while the second uses the frequency domain approach, based on low-frequency (aliasing-free part) of aliased images. Experimental results presented in this work show that the proposed algorithm reduces the computational complexities with a better accuracy compared to other subpixel registration algorithms.

Bayesian high resolution image reconstruction with incomplete multisensor low resolution systems

Abstract: We consider the problem of reconstructing a high-resolution image from an incomplete set of undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the calculation of the maximum a posteriori (MAP) estimate of the high resolution image given the low resolution observed images. We also examine the role played by the prior model when an incomplete set of low resolution images is used. Finally, the proposed method is tested on real and synthetic images.

A system to evaluate the accuracy of a visual mosaicking methodology

Abstract: When underwater vehicles navigate close to the ocean floor, computer vision techniques can be applied to obtain motion estimates. A complete system to create visual mosaics of the seabed is described in this paper. Unfortunately, the accuracy of the constructed mosaic is difficult to evaluate. The use of a laboratory setup to obtain an accurate error measurement is proposed. The system consists on a robot arm carrying a downward looking camera. A pattern formed by a white background and a matrix of black dots uniformly distributed along the surveyed scene is used to find the exact image registration parameters. When the robot executes a trajectory (simulating the motion of a submersible), an image sequence is acquired by the camera. The estimated motion computed from the encoders of the robot is refined by detecting, to subpixel accuracy, the black dots of the image sequence, and computing the 2D projective transform which relates two consecutive images. The pattern is then substituted by a poster of the sea floor and the trajectory is executed again, acquiring the image sequence used to test the accuracy of the mosaicking system.