CFA Image Research Articles

Lossless CFA image compression algorithm for wireless capsule endoscopy

Lossless CFA image compression algorithm for wireless capsule endoscopy

CFA image compression using an efficient cascaded overlapping color transformation

CFA image compression using an efficient cascaded overlapping color transformation

FiveNet: Joint image demosaicing, denoising, deblurring, super-resolution, and clarity enhancement

In this paper, a convolutional neural network for joint image demosaicing, denoising, deblurring, super-resolution and clarity enhancement is proposed. The network inputs are four-channel Bayer CFA image (R, G, G, B) and three channels of the same size containing distortions maps, namely, noise level map, blur level map, and clarity degradation map. It is shown that the designed network FiveNet can effectively process images with the mix of five different distortions. It is also demonstrated that adding clarity enhancement into the processing chain can additionally increase image quality (by up to 3-4 dB in PSNR). A small dataset ClarityDegr120 of color images with different clarity degradations and enhancements is designed using images processed by FiveNet. Mean opinion scores (MOS) for the test set are collected. The MOS prove that clarity enhancement can significantly increase image visual quality. A comparative analysis using the MOS demonstrates a low correspondence between image quality metrics and human perception for the clarity enhancement task.

Lossless White Balance for Improved Lossless CFA Image and Video Compression.

Color filter array is a spatial multiplexing of pixel-sized filters fabricated over pixel sensors in most color image sensors. The state-of-the-art lossless coding techniques of raw sensor data captured by such sensors leverage spatial or cross-color correlation using lifting schemes. In this paper, we propose a lifting-based lossless white balance algorithm. When applied to the raw sensor data, the spatial bandwidth of the implied chrominance signals decreases. We propose to use this white balance as a pre-processing step to lossless CFA subsampled image/video compression, improving the overall coding efficiency of the raw sensor data.

Hybrid compression-encryption-watermarking image algorithm based on the quaternionic wavelets transform (QWT)

In this article, we present a robust new hybrid algorithm combining successive compression, chaotic encryption, and blind watermarking images based on the quaternionic wavelets transform (QWT) to ensure the fast, simple and secure transfer of digital data. The calculations of the various evaluation parameters were carried out with the aim of determining the robustness of our algorithm against certain attacks. The results obtained of the reconstructed images before and after attacks of the compressed-encrypted-watermarked images are appreciated by calculating the evaluation parameters values and by the human visual system. The application of this hybrid algorithm on CFA images al-lowed us to obtain a stand-alone transmission system and ensure the integrity of digital data.  Â

A low complexity lossless Bayer CFA image compression

A low complexity lossless Bayer CFA image compression

NTSDCN: New Three-Stage Deep Convolutional Image Demosaicking Network

In this letter, we compose a new three-stage deep convolutional neural network (NTSDCN) for image demosaicking, and it consists of our proposed Laplacian energy-constrained local residual unit (LC-LRU) and a feature-guided prior fusion unit (FG-PFU). Specifically, the LC-LRU is used to refine the learning target of the specific residual blocks in the network and enhance the dominant information of the residual features. The FG-PFU is designed to guide the feature extraction of the red (R) and blue (B) channels by utilizing prior information from the reconstructed green (G) channel. In our proposed NTSDCN, we recover the G channel image in the first stage with the CFA image and reconstruct the R and B images in the second stage. Finally, we fine-tune the resulting R, G and B images in the third stage to compose a full-color RGB image. The experimental results show that our proposed method achieves better performance than the state-of-the-art methods. The code is available at https://github.com/wyannn/NTSDCN.

A Review on Lossless Compression Techniques for Wireless Capsule Endoscopic Data.

The videos produced during wireless capsule endoscopy have larger data size causing difficulty in transmission with limited bandwidth. The constraint on wireless capsule endoscopy hinders the performance of the compression module. The objectives of this paper are as follows: (i) to conduct an extensive review of the lossless compression techniques and (ii) to find out the limitations of the existing system and the possibilities for improvement. The literature review was conducted with a focus on the compression schemes satisfying minimum computational complexity, less power dissipation and low memory requirements for hardware implementation. A thorough study of various lossless compression techniques was conducted under two perspectives, i.e., techniques applied to Bayer CFA and RGB images. The detail of the various stages of wireless capsule endoscopy compression was investigated to have a better understanding. The suitable performance metrics for evaluating the compression techniques were listed from various literature studies. In addition to the Gastrolab database, WEO clinical endoscopy atlas and Gastrointestinal atlas were found to be better alternatives for experimentation. Pre-processing operations, especially new subsampling patterns need to be given more focus to exploit the redundancies in the images. Investigations showed that encoder module can be modified to bring more improvement towards compression. The real-time endoscopy still exists as a promising area for exploration. This review presents a research update on the details of wireless capsule endoscopy compression together with the findings as an eye-opener and guidance for further research.

Intelligent Bimodal Segmentation Applied on CFA Images of Food Products

Images segmentation is an essential step in the non-destructive analysis of food products by image analysis. It participates upstream in the operations of classification, identification or extraction of image attributes. To implement our segmentation approach, we extracted from each CFA image
 The R, G, B (for the RGB color space), and H, S, V (for the HSV color space). We then calculate the histogram of each of the color components. The component S for which the bimodal histogram was the most pronounced was selected and the binary mask was created from this component. The created binary mask has been filtered using morphological closing and opening operations. The application of each binary mask to its image CFA host, allowed us to separate with success the pixels representing the food products from the pixels of the background of the image CFA.

An Optimized Interpolation Algorithm for Bayer Pattern CFA Images based on Gradient

Digital cameras acquire images by using color filter array, each pixel contains information of only one color channel. In order to get the rest color channel information, interpolation of the image is needed. The interpolation algorithm directly affects the image quality, and most of the current algorithms have defects in some way. Some interpolation algorithms are very simple, thus causing image distortion, some perform well but are too complex to implement. This paper proposes an improved interpolation algorithm for the most widely used color filter array, Bayer array. The proposed algorithm can achieve both interpolation efficiency and interpolation effect. Based on bilinear interpolation algorithm and edge oriented interpolation algorithm, applying gradient method and color difference rule, the proposed algorithm makes full use of the correlation between color channels, and the algorithm doesn’t have great complexity. The experimental results show that the improved interpolation algorithm performs well.

An Efficient CFA Image Reconstruction Algorithm using Sparse Computation

Compressive sensing can reduce the number of measurements required for display of an image thereby, reducing storage space and balance the traffic through data communication networks. In this paper a frequency domain algorithm for efficient reconstruction of CFA Bayer filter images has been proposed. Frequency transformations will be achieved by using standard wavelet decomposition algorithms. Sparse values of CFA output image, in transformed domain will be computed using prediction algorithms (l1 magic-number gradient algorithm). Effectiveness of the algorithm will be computed using CPSNR, PSNR, MSE and IEF. This algorithm is expected to improve the visual quality of the CFA images.

Comparison of Interpolation Methods in Bayer CFA Image Compression Based on Structure Separation and APBT-JPEG

Comparison of Interpolation Methods in Bayer CFA Image Compression Based on Structure Separation and APBT-JPEG

Image Quality Evaluation on Modified Bayer CFA

Almost all digital cameras adopt a color filter array to acquire images and requesting a demosaicking process of the sub-sampled color components to have the full color image. Thus, it is necessary to restore the CFA image correctly. Otherwise, perceptible color errors are presented. This paper proposes a color interpolation algorithm based on filter. The CFA we used is modified Bayer CFA. Simulation results show that the proposed method is effective and yield high performance in CPSNR and S-CIELAB.

Performance Analysis on Yamanaka Patterned Color Filter Array

Generally, a digital camera employs a single CCD or CMOS sensor. In a color imaging device, the color information is usually obtained in sub-sampled patterns of red, green and blue pixels. Thus, full-resolution color is afterward created from this sub-sampled CFA image. This process is normally called as demosaicking. In this paper, we analyze performance of Yamanaka patterned CFA in terms of CPSNR and S-CIELAB. We show the simulation results on test images.

A New Compression Method for Bayer Color Filter Array Images

In order to improve the compression performance of Bayer CFA images exposed continuously, a new high performance remainder set near-lossless compression method is presented. Based on channel-separated-filtering, several typical Bayer CFA image compression methods are compared with the proposed remainder set algorithm. It is proved that the remainder set algorithm has not only the better compression performance, i.e., the lower bits per pixel (average about 2.16bpp), but also the better reconstructed CFA image PSNR (average about 52.31dB). Finally, the proposed method is employed in a multiple channel CMOS image sampling system and some test results are given.

A Remainder Set Near-Lossless Compression Method for Bayer Color Filter Array Images

Abstract In order to improve the compression performance of Bayer CFA images exposed continuously, a new high performance remainder set near-lossless compression method is presented. Based on channel-separated-filtering, several typical Bayer CFA image compression methods are compared with the proposed remainder set algorithm. It is proved that the remainder set algorithm has not only the better compression performance, i.e., the lower bits per pixel (average about 2.16bpp), but also the better reconstructed CFA image PSNR (average about 52.31 dB).

Erratum to: Dense stereovision using mono-CCD color cameras

Workshop, CCIW 2009, Saint-Etienne, France, March 26–27, 2009. Revised Selected Papers, edited by A. Tremeau, R. Schettini, and S. Tominaga (LNCS 5646, pp. 170–179, 2009.© Springer-Verlag Berlin Heidelberg 2009). Duplicate or redundant publication is a serious violation of scientifi c practice and is damaging to a journal or other publication. Please be reminded that it is an author’s absolute responsibility to strictly comply with rigorous scientifi c and professional standards when submitting a manuscript for publication. The editors of the journal Artifi cial Life and Robotics hereby retract the article ‘‘Dense stereovision using monoCCD color cameras’’ by Hachem Halawana, Hani Hamdan, and Mohamad Hamdan, which appeared in Artif Life Robotics (2010) 15:508–511. The article is being retracted because it contained the same research data used in the chapter titled ‘‘Color Stereo Matching Cost Applied to CFA Images’’ by Hachem Halawana, Ludovic Macaire, and Francois Cabestaing, published in Computational Color Imaging Second International Erratum to: Artif Life Robotics (2010) 15:508–511 DOI 10.1007/s10015-010-0853-0

베이어 CFA 카메라를 사용한 다중 스펙트럼 기반 컬러영상 생성 기술

본 논문에서는 기존의 베이어 컬러필터 배열 기반의 영상 센서를 사용하는 카메라에서 개선된 컬러 영상을 생성하기 위한 영상 시스템을 제안한다. 제안한 컬러 영상 생성 기술은 두 장의 연속적인 영상(광대역 G채널 필터를 사용한 영상과 색필터를 적용하지 않고 연속적으로 촬영한 영상)으로부터 RGBCY의 다양한 색 정보를 추출하여 디모자이킹을 위한 컬러 영상 보간의 계산은 감소시키며 개선된 색상 정보를 가지는 영상을 얻을 수 있다. 또한 기존의 베이어 CFA 영상센서를 사용하기 때문에 새로운 형태의 CFA를 제작할 필요 없이 다중 스펙트럼 영상을 취득할 수 있는 카메라 시스템으로 확장 가능하다. 마지막으로 본 논문에서 제안한 컬러영상 생성을 위한 카메라 시스템의 성능을 검증하기 위해서 기존의 디모자이킹 기술과 비교하였으며, 그 결과 컬러 중첩 현상이 상당히 개선되었음을 보였다. This paper presents an imaging system for reconstruction of enhanced color images using the conventional Bayer CFA. By extracting various colors such as RGBCY from two sequential images which consist of a image by broadband G channel lens filter and the other image captured without one, the proposed color image reconstruction system can reduce the computational complexity for demosaicking and make high resolution color information without aliasing artifacts. Because the proposed system uses the common Bayer CFA image sensor, fabricating a new type of CFA is not necessary for obtaining a multi-spectral image, which can be easily extensible for applications of multi-spectral imaging. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing demosaicking methods, the proposed camera system showed the significant improvements in the sense of color resolution.