A method of color filter array interpolation with alias cancellation properties

ABSTRACT Digital still cameras use a single charge-coupled device (CCD) sensor array and a color filter array (CFA) to sample a full-color image. Thus, the measured image is an interleaving of the subsampled red, green, and blue images. The red and blue images are sampled at a lower rate, so if standard interpolation techniques are used, the reconstructed red and blue images will be missing some highfrequency information and could contain distortions from aliasing. This paper proposes a method of CFA interpolation that combines information from the green image with the subsampled red and blue images to attack these problems. The green high-frequency information is added to the interpolated red and blue images to increase the sharpness of the output and is also used to estimate the aliasing in the interpolated red and blue images, providing a means of reducing the appearance of the aliasing distortions

Deep Residual Network for Joint Demosaicing and Super-Resolution

In digital photography, two image restoration tasks have been studied extensively and resolved independently: demosaicing and super-resolution. Both these tasks are related to resolution limitations of the camera. Performing super-resolution on a demosaiced images simply exacerbates the artifacts introduced by demosaicing. In this paper, we show that such accumulation of errors can be easily averted by jointly performing demosaicing and super-resolution. To this end, we propose a deep residual network for learning an end-to-end mapping between Bayer images and high-resolution images. By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately. We perform extensive experiments to show that our deep residual network achieves demosaiced and super-resolved images that are superior to the state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics

Solid State Television Camera (CID)

The design, development and test are described of a charge injection device (CID) camera using a 244x248 element array. A number of video signal processing functions are included which maximize the output video dynamic range while retaining the inherently good resolution response of the CID. Some of the unique features of the camera are: low light level performance, high S/N ratio, antiblooming, geometric distortion, sequential scanning and AGC

A three-dimensional simulation of transition and early turbulence in a time-developing mixing layer

The physics of the transition and early turbulence regimes in the time developing mixing layer was investigated. The sensitivity of the mixing layer to the disturbance field of the initial condition is considered. The growth of the momentum thickness, the mean velocity profile, the turbulence kinetic energy, the Reynolds stresses, the anisotropy tensor, and particle track pictures of computations are all examined in an effort to better understand the physics of these regimes. The amplitude, spectrum shape, and random phases of the initial disturbance field were varied. A scheme of generating discrete orthogonal function expansions on some nonuniform grids was developed. All cases address the early or near field of the mixing layer. The most significant result shows that the secondary instability of the mixing layer is produced by spanwise variations in the straining field of the primary vortex structures

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image

Application of multirate digital signal processing to image compression

With the increasing emphasis on digital communication and digital processing of images and video, image compression is drawing considerable interest as a means of reducing computer storage and communication channels bandwidth requirements. This thesis presents a method for the compression of grayscale images which is based on the multirate digital signal processing system. The input image spectrum is decomposed into octave wide subbands by critically resampling and filtering the image using separable FIR digital filters. These filters are chosen to satisfy the perfect reconstruction requirement. Simulation results on rectangularly sampled images (including a text image) are presented. Then, the algorithm is applied to the hexagonally resampled images and the results show a slight increase in the compression efficiency. Comparing the results against the standard (JPEG), indicate that this method does not have the blocking effect of JPEG and it preserves the edges even in the presence of high noise level