Defective Pixels Research Articles

Edge preservation of impulse noise filtered images by improved anisotropic diffusion

This paper provides a robust scheme for random valued impulsive noise reduction along with edge preservation by anisotropic diffusion with improved diffusivity. The defective impulse noisy pixels are detected by Laplacian based second order pixel difference operation where these defective pixels are replaced by appropriate values with regard of the gray level of their four directional neighbors. This de-noised image undergoes the diffusion operation where diffusion coefficient function is modified to make it adaptive by incorporating local gray level variance information. The proposed modified diffusion scheme effectively restore the edges and fine details destroyed during impulse noise reduction process. The effect of proposed diffusion scheme has been studied on various images and the results are compared with some existing diffusion methods which are independently used for impulse noise reduction and edge preservation. The results shows that the prior removal of impulsive noise before the application of diffusion process is advantageous over the direct application of diffusion for removing the impulsive noise. In addition, the results of the proposed diffusion scheme are compared with some of the median filter based methods which are effectively used for impulse noise reduction without caring of edge preservation. The proposed diffusion scheme sufficiently preserves the edges without boosting of impulsive noise components on images corrupted up to 50 % of the impulsive noise density.

2014 CAN CT (COMPUTED TOMOGRAPHY) VIRTUAL CYSTOSCOPY WITH INTRA VESICAL DILUTE CONTRAST MEDIA REPLACE SURVEILLANCE CYSTOSCOPY?

You have accessJournal of UrologyImaging/Radiology: Uroradiology (I)1 Apr 20132014 CAN CT (COMPUTED TOMOGRAPHY) VIRTUAL CYSTOSCOPY WITH INTRA VESICAL DILUTE CONTRAST MEDIA REPLACE SURVEILLANCE CYSTOSCOPY? Uttam Mete, abizer kapadia, anupam lal, niranjan khandelwal, Nandita Kakkar, and Arupkumar Mandal Uttam MeteUttam Mete Chandigarh, India More articles by this author , abizer kapadiaabizer kapadia Chandigarh, India More articles by this author , anupam lalanupam lal Chandigarh, India More articles by this author , niranjan khandelwalniranjan khandelwal Chandigarh, India More articles by this author , Nandita KakkarNandita Kakkar Chandigarh, India More articles by this author , and Arupkumar MandalArupkumar Mandal Chandigarh, India More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2433AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES To determine the value of Computed tomography (CT) Virtual Cystoscopy by intravesical instillation of dilute contrast medium, for the detection of bladder tumour in patients undergoing conventional cystoscopy for Transitional Cell Carcinoma (TCC) of the Urinary Bladder. METHODS In this prospective study a total of twenty (20) patients, comprising of 18 males and two females, with carcinoma of the urinary bladder were enrolled. After performing non-enhanced scan, 250 to 300 ml of dilute contrast(10 ml of 76% urogaffin contrast in 500 ml of normal saline, 2% solution) was instilled into the bladder. Helical volume scanning of the contrast medium filled baldder was done in both supine and prone positions. Three-dimensional images were reconstructed with visualization gray scale software to study the axial images. Data was downloaded into a work station equipped with navigation software. Bladder mucosal surface was delineated using contrast medium and by adjusting the threshold HU manually using standard visualization software. The lumen was displayed without any pixel defects by optimizing the threshold at 300 to 450 HU based on smoothing pixel defects visually. The number, size, location and morphological features of the lesions were noted on transverse and virtual images. Each lesion was characterized as polypoidal, sessile, focal septae or irregular wall thickening. Data acquired by virtual CT Cystoscopy and conventional cystoscopy were compared. RESULTS The conventional cystoscopy had detected 17 lesions in 14 patients (70 %). CT virtual cystoscopy detected fifteen lesions in 12patients (60%) and fifteen out of 17 lesions (88.24%). Seven lesions (58.3 %) were papillary and 5 (41.6 %) were sessile. (25 %) patients had multiple lesion and (75 %) had a single lesion. The mean lesion size was 2.8 cm. ( smallest 0.9 cm & largest 5.5cm). Two lesion with <1cm. were missed. The probable reason of not achieving 100 % sensitivity was the lesion size. The specificity, positive predictive value, negative predictive value and accuracy of CT cystoscopy were 100%, 100%, 75% & 90% respectively. CONCLUSIONS Detection rate of bladder lesion with vitual CT cystoscopy approaches conventional cystoscopy . It obviates operation theatre visit, physical & psychological trauma and might detect enlarged pelvic lymph nodes. In situations where conventional cystoscopy cannot be applied CT virtual cystoscopy can be of clinical benefit. CT machine with proper approved software for navigation and an experienced radiologist are the prerequisite for CT cystoscopy. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e827 Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information Uttam Mete Chandigarh, India More articles by this author abizer kapadia Chandigarh, India More articles by this author anupam lal Chandigarh, India More articles by this author niranjan khandelwal Chandigarh, India More articles by this author Nandita Kakkar Chandigarh, India More articles by this author Arupkumar Mandal Chandigarh, India More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Fabric defect detection using adaptive dictionaries

In this paper, we present a new fabric defect detection algorithm based on learning an adaptive dictionary. Such a dictionary can efficiently represent columns of normal fabric images using a linear combination of its elements. Benefiting from the fact that defects on a fabric appear to be small in size, a dictionary can be learned directly from a testing image itself instead of a reference, allowing more flexibility to adapt to varying fabric textures. When modeling a test image using the learned dictionary, columns involving anomalies of the test image are likely to have larger reconstruction errors than normal ones. The anomalous regions (defects) can be easily enhanced in the residual image. Then, a simple threshold operation is able to segment the defective pixels from the residual image. To adapt more defects, especially some linear defects, we rotate the test image by a slight degree and re-analyze the rotated image. Compared to the Fourier method, experimental results on 47 real-world test images with defects reveal that our algorithm is able to adapt to varying fabric textures and exhibits more accurate defect detection.

Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone-beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system.

SENTINEL-2 LEVEL 1 PRODUCTS AND IMAGE PROCESSING PERFORMANCES

Abstract. In partnership with the European Commission and in the frame of the Global Monitoring for Environment and Security (GMES) program, the European Space Agency (ESA) is developing the Sentinel-2 optical imaging mission devoted to the operational monitoring of land and coastal areas. The Sentinel-2 mission is based on a satellites constellation deployed in polar sun-synchronous orbit. While ensuring data continuity of former SPOT and LANDSAT multi-spectral missions, Sentinel-2 will also offer wide improvements such as a unique combination of global coverage with a wide field of view (290 km), a high revisit (5 days with two satellites), a high resolution (10 m, 20 m and 60 m) and multi-spectral imagery (13 spectral bands in visible and shortwave infra-red domains). In this context, the Centre National d'Etudes Spatiales (CNES) supports ESA to define the system image products and to prototype the relevant image processing techniques. This paper offers, first, an overview of the Sentinel-2 system and then, introduces the image products delivered by the ground processing: the Level-0 and Level-1A are system products which correspond to respectively raw compressed and uncompressed data (limited to internal calibration purposes), the Level-1B is the first public product: it comprises radiometric corrections (dark signal, pixels response non uniformity, crosstalk, defective pixels, restoration, and binning for 60 m bands); and an enhanced physical geometric model appended to the product but not applied, the Level-1C provides ortho-rectified top of atmosphere reflectance with a sub-pixel multi-spectral and multi-date registration; a cloud and land/water mask is associated to the product. Note that the cloud mask also provides an indication about cirrus. The ground sampling distance of Level-1C product will be 10 m, 20 m or 60 m according to the band. The final Level-1C product is tiled following a pre-defined grid of 100x100 km2, based on UTM/WGS84 reference frame. The stringent image quality requirements are also described, in particular the geo-location accuracy for both absolute (better than 12.5 m) and multi-temporal (better than 0.3 pixels) cases. Then, the prototyped image processing techniques (both radiometric and geometric) will be addressed. The radiometric corrections will be first introduced. They consist mainly in dark signal and detector relative sensitivity correction, crosstalk correction and MTF restoration. Then, a special focus will be done on the geometric corrections. In particular the innovative method of automatic enhancement of the geometric physical model will be detailed. This method takes advantage of a Global Reference Image database, perfectly geo-referenced, to correct the physical geometric model of each image taken. The processing is based on an automatic image matching process which provides accurate ground control points between a given band of the image to refine and a reference image, allowing to dynamically calibrate the viewing model. The generation of the Global Reference Image database made of Sentinel-2 pre-calibrated mono-spectral images will be also addressed. In order to perform independent validation of the prototyping activity, an image simulator dedicated to Sentinel-2 has been set up. Thanks to this, a set of images have been simulated from various source images and combining different acquisition conditions and landscapes (mountains, deserts, cities …). Given disturbances have been also simulated so as to estimate the end to end performance of the processing chain. Finally, the radiometric and geometric performances obtained by the prototype will be presented. In particular, the geo-location performance of the level-1C products which widely fulfils the image quality requirements will be provided.

Archive Film Defect Detection and Removal: An Automatic Restoration Framework

In this paper, we present an automatic restoration system targeting on dirt and blotches in digitized archive films. The system is composed of mainly two modules: defect detection and defect removal. In defect detection, we locate the defects by combing temporal and spatial information across a number of frames. An HMM is trained for normal observation sequences and then applied within a framework to detect defective pixels. The resulting defect maps are refined in a two-stage false alarm elimination process and then passed over to the defect removal procedure. A labelled (degraded) pixels is restored in a multiscale framework by first searching the optimal replacement in its dynamically generated, random walk based region of candidate pixel-exemplars and then updating all its features (intensity, motion and texture). Finally, the proposed system is compared against state-of-the-art methods to demonstrate improved accuracy in both detection and restoration using synthetic and real degraded image sequences.

Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error

Data acquisition errors due to dead pixels or other hardware defects can cause undesirable artifacts in imaging applications. Compensating for these defects typically requires knowledge such as a defective pixel map, which can be difficult or costly to obtain and which is not necessarily static. However, recent calibration data is readily available in many applications. In this paper, we compute optimal filters for image deconvolution with denoising using only this calibration data, by minimizing the empirical Bayes risk. We derive a bound on how the reconstruction changes as the number of dead pixels grows. We show that our approach is able to reconstruct missing information better than standard filtering approaches and is robust even in the presence of a large number of defects and to defects that arise after calibration.

Identification and replacement of defective pixel based on Matlab for IR sensor

Infrared focal plane arrays (IRFPAs) usually contain many defective pixels. These defective pixels have to be corrected because those can significantly impair the performance of infrared image of IRFPAs. As is known to all, infrared image acquisition and analysis based on Matlab can be helpful to identify and correct defective pixels. In this paper, we proposed a novel method to identify and correct defective pixels. In the phase of identification, the defective pixels could be identified by the algorithms combined with median filtering algorithm and improved standard deviation algorithm. In the phase of correction, proportion-spatial defective pixel replacement (PSDPR) algorithm was introduced to replace the defective pixels, and this method reduced the difficulty of replacement originating from the clustering phenomenon of defective pixels. In addition, an experiment of verification was done, and showed the proposed scheme worked effectively.

TFT-LCD용 TFT기판에서 저에너지 전자빔을 이용한 전기적 결함 검출 메카니즘 분석

TFT-LCD용 TFT기판 상에서 저에너지 마이크로 컬럼을 이용하는 전기적인 결함 검출 메카니즘을 분석하였다. 본 연구에서는 고진공 챔버 내에서 7인치 TFT 기판에 저에너지 전자빔을 주사하여 여러 가지 불량 화소에 대한 SEM 이미지를 획득하였다. 더불어 각각의 불량 화소에서 나타나는 현상과 전기적인 거동과의 연관성을 분석하여 검출 메카니즘을 해석하였다. 그 결과로서 저에너지 초소형 전자 컬럼을 이용하는 저에너지 전자빔에 의한 SEM 이미지는 전자간 반발효과에 크게 영향을 받는 일관성 있는 결과를 확인하였다. We have analyzed the electrical defect detection mechanism using low energy microcolumn on the TFT substrate for TFT-LCD. In this study, we have acquired the SEM images of the various pixel defects for 7-inch TFT substrate by scanning of low energy electron beam in the high vacuum chamber. Futhermore, we have interpreted the defect detection mechanism through the correlations between the SEM images and electrical behaviors of the defective pixels. As a result, we obtained consistent results as the follows. We can confirm that the SEM images using low energy electron beam are significantly affected by the space charge effect.

Classification of ring artifacts for their effective removal using type adaptive correction schemes

High resolution tomographic images acquired with a digital X-ray detector are often degraded by the so called ring artifacts. In this paper, a detail analysis including the classification, detection and correction of these ring artifacts is presented. At first, a novel idea for classifying rings into two categories, namely type I and type II rings, is proposed based on their statistical characteristics. The defective detector elements and the dusty scintillator screens result in type I ring and the mis-calibrated detector elements lead to type II ring. Unlike conventional approaches, we emphasize here on the separate detection and correction schemes for each type of rings for their effective removal. For the detection of type I ring, the histogram of the responses of the detector elements is used and a modified fast image inpainting algorithm is adopted to correct the responses of the defective pixels. On the other hand, to detect the type II ring, first a simple filtering scheme is presented based on the fast Fourier transform (FFT) to smooth the sum curve derived form the type I ring corrected projection data. The difference between the sum curve and its smoothed version is then used to detect their positions. Then, to remove the constant bias suffered by the responses of the mis-calibrated detector elements with view angle, an estimated dc shift is subtracted from them. The performance of the proposed algorithm is evaluated using real micro-CT images and is compared with three recently reported algorithms. Simulation results demonstrate superior performance of the proposed technique as compared to the techniques reported in the literature.

Development of quality control system for flat-panel detectors

The characteristics of flat-panel detectors (FPD) are degraded by exposure to radiation. Degradation in a FPD progresses locally and has a nonlinear relationship to the radiation dose. In order to manage FPD systems properly, one must perform quality control (QC) such as evaluation of image degradation. However, no evaluation method for degradation has been established. In this paper, we first review the structure and degradation mechanism of FPDs, and then we propose a daily QC system for FPDs. To evaluate the degradation of FPDs, we investigated the number of defective pixels and lines, as well as the offset level of the pixel output. Furthermore, we developed daily QC software for FPD that can evaluate the image quality and is operationally simple. In the experiments, an indirect-conversion type FPD was evaluated by our proposed system. The offset level of FPD increased exponentially with X-ray exposure; no trends were seen for the number of defective pixels and defective lines. The required time for the evaluation of an FPD was about 1min, and no special skills were needed for the analysis. These results indicate that our system may be useful for daily QC of FPDs.

Defective pixel map creation based on wavelet analysis in digital radiography detectors

The application of digital radiography detectors has attracted increasing attention in both medicine and industry. Since the imaging detectors are fabricated by semiconductor manufacturing process over large areas, defective pixels in the detectors are unavoidable. Moreover, the radiation damage due to the routine use of the detectors progressively increases the density of defective pixels. In this study, we present a method of identifying defective pixels in digital radiography detectors based on wavelet analysis. Artifacts generated due to wavelet transformations have been prevented by an additional local threshold method. The proposed method was applied to a sample digital radiography and the result was promising. The proposed method uses a single pair of dark and white images and does not require them to be corrected in gain-and-offset properties. This method will be helpful for the reliable use of digital radiography detectors through the working lifetime.

Design of a Smart CMOS Readout Circuit for Panoramic X-Ray Time Delay and Integration Arrays

This paper presents a novel charge transfer CMOS readout circuit for an X-ray time delay and integration (TDI) array with a depth of 64. In this study, a charge transfer readout scheme based on CMOS technology is proposed to sum 64 stages of the TDI signal. In addition, a dead pixel elimination circuit is integrated within a chip, thus resolving the weakness of TDI arrays related to defective pixels. The proposed method is a novel CMOS solution for large depth TDI arrays. Thus, a high signal-to-noise ratio (SNR) can be acquired due to the increased TDI depth. The readout chip was fabricated with a 0.6µm standard CMOS process for a 150×64 CdTe X-ray detector array. The readout circuit was found to effectively increase the charge storage capacity up to 1.6×108 electrons, providing an improved SNR by a factor of approximately 8. The measured equivalent noise charge resulting from the readout circuit was 1.68×104 electrons, a negligible value compared to the shot noise from the detector.

Measuring the effectiveness of bad pixel detection algorithms using the ROC curve

Digital camera sensors usually contain defective pixels that either arise during the manufacturing process, or develop over time. These defects though small in number, are very noticeable to an experienced eye when looking at images captured from the camera. As a result it becomes extremely important for the sensor manufacturing companies to develop methods which would reduce these manufacturing errors. There have been a number of algorithms proposed for online detection of sensor defects in the literature, but there is no standard method of fairly comparing algorithms given that each algorithm uses thresholds in a different way. This paper presents a novel idea which makes use of the Receiver Operating Characteristics (ROC) curve for comparison among the various bad pixel correction algorithms. We demonstrate how the characteristics of the ROC curve can be used for determining the effectiveness of the algorithms and hence help us to decide which of the algorithms perform better than the others. Experimental results comparing two such bad pixel detection algorithms are provided.

Laser-induced photoacoustic imaging for breast cancer detection using multivariate image analysis.

Abstract:Time-resolved photoacoustic imaging has been used to characterize breast tissue for the purpose of discriminating between normal and maligned areas of the tissue. Ultrasonic thermoelastic waves were generated in breast tissue by the absorption of nanosecond laser pulses at 193 nm produced by Q-switched Excimer laser in conjunction with a Michelson interferometer used to detect the thermoelastic waves and plot the 2-D and the 3-D image through IR- CCD. The concepts behind the use of photo-acoustic techniques for off-line detection of breast cancer tumor features were presented in earlier research papers [1][2]. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of tumor features of breast cancer. MIA is used to rapidly detect the presence and quantity of common tumor features as they scanned by an RGB camera. Multiway principal component analysis is used to decompose the acquired three-channel tumor images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of tumor defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early tumor detection. The proposed technique can potentially be used on-line to prescreen the existence of tumors through vision based systems.

Two-Dimensional Long-Wavelength and Very Long-Wavelength Focal-Plane Arrays at AIM

In recent years AIM has expanded its portfolio of standard infrared (IR) focal-plane arrays in the 3 μm to 5 μm (mid-wavelength infrared, MWIR) and 8 μm to 10 μm (long-wavelength infrared, LWIR) spectral ranges with two-dimensional IR detectors, sensitive in the 0.9 μm to 2.5 μm (short-wavelength infrared, SWIR) and especially in the 10 μm to 15 μm (very long-wavelength infrared, VLWIR) spectral ranges. This paper reports on the latest technological advancements that will benefit not only prototype applications for which they are demonstrated but a wide range of AIM products. A reduction of the pixel pitch from 24 μm to 15 μm is the result of increasing demands for compact detection modules with reduced weight, size, power consumption, and cost efficiency. Performance characterization for such a reduced-pitch 640 × 512 module in the LWIR (cut-off 10.5 μm at 67 K) yields mean noise equivalent temperature difference of 32.2 mK and defective pixel rate of only 0.5%. Extending the detection wavelength further into the VLWIR is of major interest for space applications such as the Meteosat Third Generation, which poses challenging requirements for sensor material homogeneity and dark-current density. To meet this requirement, an extrinsic doping approach is utilized on a 256 × 256 mercury cadmium telluride (MCT) focal-plane array with ∼14 μm cut-off wavelength at 55 K operating temperature, and a dark- current density of about 1 pA/μm2 is demonstrated.

First qualification and selection of the eROSITA PNCCDs

For the X-ray astronomy instrument eROSITA a framestore PNCCD was developed by the MPI Halbleiterlabor. The PNCCD has an image area of 384 × 384 pixels with a size of 75 μ m × 75 μ m . Each channel of the PNCCD has an own readout anode which allows parallel amplification and signal processing of the CCD signals of one row. The first measurements for the spectroscopic characterization of the PNCCDs are made with a special measurement setup—the so-called Cold Chuck Probe Station. The Cold Chuck Probe Station allows to fully operate the CCD without mounting and bonding the chip on a PCB as the CCD is contacted only with needles. Thus all eROSITA PNCCDs can be qualified under the same measurement conditions and with an identical electronic setup. Therefore the results can be compared directly. The spectroscopic properties of the PNCCDs, like the charge transfer efficiency and the energy resolution are measured. Also pixel defects such as bright pixels or non-transferring pixels are detected. With the Cold Chuck Probe Station a readout noise of 2.7 e − ENC can be achieved and reliable measurement results obtained. Based on these results the best PNCCDs will be selected for eROSITA.

Algorithms for Masking Pixel Defects at Low Exposure Conditions for CMOS Image Sensors

This paper introduces certain innovative algorithms to mask for pixel defects seen in image sensors. Pixel defectivity rates scale with pixel architecture and process nodes. Smaller pixel and process nodes introduce more defects in manufacturing. Brief introduction to causes for pixel defectivity at lower pixel nodes is explained. Later in the paper, popular defect correction schemes used in image processing applications are discussed. A new approach for defect correction is presented and evaluated using images captured from an 8M Bayer image sensor. Experimentation for threshold evaluation is done and presented with practical results for better optimization of proposed algorithms. Experimental data shows that proposed defect corrections preserves a lot of edge details and corrects for bright and hot pixels/clusters, which are evaluated using histogram analysis.

A NOVEL METHOD FOR DETECTING AND CORRECTING CCD IMAGE MALFUNCTION PIXELS BASED ON SPATIAL AND TEMPORAL STATISTICS

Due to inevitable manufacture defects,there exist a lot of malfunction pixels in the CCD image.The traditional detecting method must require a series of sample images for standard objects taken under a specific condition.Starting with an analysis of electro-optical response characteristics of the CCD pixel,this paper classifies the detected malfunction pixels and makes a statistical analysis on a series of images taken in different scenes.On such a basis,the defective pixels are detected and corrected.A study of a series of aerial CCD images has proved the efficiency of this new method.

Ubiquitous 환경 하에서 고장 극복 암호 및 데이터 압축

본 논문은 암호화된 영상 데이터가 유비쿼터스 환경 하에서 무선 간섭에 의한 랜덤 오류를 가질 때 복호화 과정의 오류 산사태에 대한 해결책을 제시하였다. 영상 획득 장치는 영상 압축과 암호화 기능을 가지고서 데이터 트래픽 양을 줄이고 개인 정보를 보호하도록 구성한다. 블록 암호 알고리즘은 암호문의 단일 비트 오류가 여러 개의 픽셀 결함을 유발하는 산사태 효과를 겪을 수 있다. 새로운 고장 극복 방식은 오류의 산사태 효과를 다루는데 3 차원 데이터 셔플을 활용하여 에러 비트를 여러 프레임으로 분산시켜서 고립된 영상 결함으로 나타나도록 한다. 인접 화소 값에 대한 평균화 또는 다수결 회로는 에러정정을 위한 데이터 증가 없이 두드러져 보이는 화소 결함을 극복하도록 한다. 이 방식은 기존 Hamming code 방식보다 33% 적은 데이터 트래픽 부하를 가진다. This paper presents a solution to error avalanche of deciphering where radio noise brings random bit errors in encrypted image data under ubiquitous environment. The image capturing module is to be made comprising data compression and encryption features to reduce data traffic volume and to protect privacy. Block cipher algorithms may experience error avalanche: multiple pixel defects due to single bit error in an encrypted message. The new fault tolerant scheme addresses error avalanche effect exploiting a three-dimensional data shuffling process, which disperses error bits on many frames resulting in sparsely isolated errors. Averaging or majority voting with neighboring pixels can tolerate prominent pixel defects without increase in data volume due to error correction. This scheme has 33% lower data traffic load with respect to the conventional Hamming code based approach.