Unsharp Masking Sharpening Detection Via Overshoot Artifacts Analysis Gang Cao, Yao Zhao, Member, IEEE, Rongrong Ni, Member, IEEE,Andalexc.Kot, Fellow, IEEE
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Sharpening Scanned Images
Sharpening scanned images Shortly after the launch of Photoshop CS3, Adobe released a Camera Raw 4.1 update which greatly enhanced the capture sharpening capabilities in Camera Raw. In my view, this new feature changed the Photoshop workflow completely, such that everything I had written in my CS3 book about sharpening was immediately out-of-date. As you will have read in the book, I now advocate using Camera Raw to capture sharpen all your raw images and there is also no reason why you can’t also use Camera Raw capture sharpening to sharpen (as yet unsharpened) scanned TIFF images. Having said that, I do realize that some people may prefer to carry out everything in Photoshop. For those readers I have made the Photoshop capture sharpening method instructions still available in this PDF. Image sharpening It is an unavoidable fact that image detail will progressively become lost at critical stages of the digital image making process. Without corrective sharpening, a printed image will appear softer than expected. This is by no means a new phenomenon or one that is unique to digital imaging. The problems begin as soon as you focus the subject in your viewfinder and press the shutter release. The first variable is the camera lens. Most photographers are all too aware that cheaper inferior lenses will produce less sharp pictures, but the film emulsion can also influence the sharpness, as can the quality of the digital sensor and the number of photosites on the sensor chip. If you are shooting film, then you will have to scan a chrome transparency, negative or photographic print made with a darkroom enlarger (which involves yet another optical step). -
Automatic Detection of Perceived Ringing Regions in Compressed Images
International Journal of Electronics and Communication Engineering. ISSN 0974-2166 Volume 4, Number 5 (2011), pp. 491-516 © International Research Publication House http://www.irphouse.com Automatic Detection of Perceived Ringing Regions in Compressed Images D. Minola Davids Research Scholar, Singhania University, Rajasthan, India Abstract An efficient approach toward a no-reference ringing metric intrinsically exists of two steps: first detecting regions in an image where ringing might occur, and second quantifying the ringing annoyance in these regions. This paper presents a novel approach toward the first step: the automatic detection of regions visually impaired by ringing artifacts in compressed images. It is a no- reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). To maintain low complexity for real-time applications, the proposed approach adopts a perceptually relevant edge detector to capture regions in the image susceptible to ringing, and a simple yet efficient model of visual masking to determine ringing visibility. Index Terms: Luminance masking, per-ceptual edge, ringing artifact, texture masking. Introduction In current visual communication systems, the most essential task is to fit a large amount of visual information into the narrow bandwidth of transmission channels or into a limited storage space, while maintaining the best possible perceived quality for the viewer. A variety of compression algorithms, for example, such as JPEG[1] and MPEG/H.26xhave been widely adopted in image and video coding trying to achieve high compression efficiency at high quality. These lossy compression techniques, however, inevitably result in various coding artifacts, which by now are known and classified as blockiness, ringing, blur, etc. -
Generalized Unsharp Masking Algorithm for Contrast and Sharpness
International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 2, Issue 9 (September 2013), PP. 73-90 Generalized Unsharp Masking Algorithm for Contrast And Sharpness Bharathi1,N Santosh2,A Sonthosh3 1,2,3 ECE Dept, Sree Dattha Institute of Engineering & Science ABSTRACT: In the applications like medical radiography enhancing movie features and observing the planets it is necessary to enhance the contrast and sharpness of an image. The model propose a generalized unsharp masking algorithm using the exploratory data model as a unified framework. The proposed algorithm is designed as to solve simultaneously enhancing contrast and sharpness by means of individual treatment of the model component and the residual, reducing the halo effect by means of an edge-preserving filter, solving the out of range problem by means of log ratio and tangent operations. Here is a new system called the tangent system which is based upon a specific bargeman divergence. Experimental results show that the proposed algorithm is able to significantly improve the contrast and sharpness of an image. Using this algorithm user can adjust the two parameters the contrast and sharpness to have desired output Index Terms: Bregman divergence, exploratory data model, generalized linear system, image enhancement, unsharp masking. I. INTRODUCTION A digital gray image is a simple two dimensional matrix of numbers ranging from 0 to 255. These numbers represents different shades of gray. The number „0‟ represents pure black color and number „255‟ represents pure white color. 1.1 Image Enhancement The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. -
Analysis of Ringing Artifact in Image Fusion Using Directional Wavelet
Special Issue - 2021 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 NTASU - 2020 Conference Proceedings Analysis of Ringing Artifact in Image Fusion Using Directional Wavelet Transforms y z x Ashish V. Vanmali Tushar Kataria , Samrudhha G. Kelkar , Vikram M. Gadre Dept. of Information Technology Dept. of Electrical Engineering Vidyavardhini’s C.O.E. & Tech. Indian Institute of Technology, Bombay Vasai, Mumbai, India – 401202 Powai, Mumbai, India – 400076 Abstract—In the field of multi-data analysis and fusion, image • Images taken from multiple sensors. Examples include fusion plays a vital role for many applications. With inventions near infrared (NIR) images, IR images, CT, MRI, PET, of new sensors, the demand of high quality image fusion fMRI etc. algorithms has seen tremendous growth. Wavelet based fusion is a popular choice for many image fusion algorithms, because of its We can broadly classify the image fusion techniques into four ability to decouple different features of information. However, categories: it suffers from ringing artifacts generated in the output. This 1) Component substitution based fusion algorithms [1]–[5] paper presents an analysis of ringing artifacts in application of 2) Optimization based fusion algorithms [6]–[10] image fusion using directional wavelets (curvelets, contourlets, non-subsampled contourlets etc.). We compare the performance 3) Multi-resolution (wavelets and others) based fusion algo- of various fusion rules for directional wavelets available in rithms [11]–[15] and literature. The experimental results suggest that the ringing 4) Neural network based fusion algorithms [16]–[19]. artifacts are present in all types of wavelets with the extent of Wavelet based multi-resolution analysis decouples data into artifact varying with type of the wavelet, fusion rule used and levels of decomposition. -
2D Signal Processing (III): 2D Filtering
6.003 Signal Processing Week 11, Lecture A: 2D Signal Processing (III): 2D Filtering 6.003 Fall 2020 Filtering and Convolution Time domain: Frequency domain: 푋[푘푟, 푘푐] ∙ 퐻[푘푟, 푘푐] = 푌[푘푟, 푘푐] Today: More detailed look and further understanding about 2D filtering. 2D Low Pass Filtering Given the following image, what happens if we apply a filter that zeros out all the high frequencies in the image? Where did the ripples come from? 2D Low Pass Filtering The operation we did is equivalent to filtering: 푌 푘푟, 푘푐 = 푋 푘푟, 푘푐 ∙ 퐻퐿 푘푟, 푘푐 , where 2 2 1 푓 푘푟 + 푘푐 ≤ 25 퐻퐿 푘푟, 푘푐 = ቊ 0 표푡ℎ푒푟푤푠푒 2D Low Pass Filtering 2 2 1 푓 푘푟 + 푘푐 ≤ 25 Find the 2D unit-sample response of the 2D LPF. 퐻퐿 푘푟, 푘푐 = ቊ 0 표푡ℎ푒푟푤푠푒 퐻 푘 , 푘 퐿 푟 푐 ℎ퐿 푟, 푐 (Rec 8A): 푠푛 Ω 푛 ℎ 푛 = 푐 퐿 휋푛 Ω푐 Ω푐 The step changes in 퐻퐿 푘푟, 푘푐 generated overshoot: Gibb’s phenomenon. 2D Convolution Multiplying by the LPF is equivalent to circular convolution with its spatial-domain representation. ⊛ = 2D Low Pass Filtering Consider using the following filter, which is a circularly symmetric version of the Hann window. 2 2 1 1 푘푟 + 푘푐 2 2 + 푐표푠 휋 ∙ 푓 푘푟 + 푘푐 ≤ 25 퐻퐿2 푘푟, 푘푐 = 2 2 25 0 표푡ℎ푒푟푤푠푒 2 2 1 푓 푘푟 + 푘푐 ≤ 25 퐻퐿1 푘푟, 푘푐 = ቐ Now the ripples are gone. 0 표푡ℎ푒푟푤푠푒 But image more blurred when compare to the one with LPF1: With the same base-width, Hann window filter cut off more high freq. -
Handout 04 Filtering in the Frequency Domain
Digital Image Processing (420137), SSE, Tongji University, Spring 2013, given by Lin ZHANG Handout 04 Filtering in the Frequency Domain 1. Basic Concepts (1) Any periodic functions, no matter how complex it is, can be represented as the sum of a series of sine and cosine functions with different frequencies. (2) The signal’s frequency components can be clearly observed in the frequency domain. (3) Fourier transform is invertible. That means after the Fourier transform and the inverse Fourier transform, no information will be lost. (4) Usually, the Fourier transform F(u) is a complex number, from which we can define its magnitude, power spectrum, and the phase angle. (5) The impulse function, or Dirac function, is widely used in theoretical analysis of linear systems. It can be considered as an infinitely high, infinitely thin spike at the origin, with total area one under the spike. (6) Dirac function has a sift property, defined as f ()tttdtft (00 ) ( ). (7) The basic theorem linking the spatial domain to the frequency domain is the convolution theorem, ft()*() ht F ( ) H ( ). (8) The Fourier transform of a Gaussian function is also of a Gaussian shape in the frequency domain. (9) For visualization purpose, we often use fftshift to shift the origin of an image’s Fourier transform to its center position. (10) The basic steps for frequency filtering comprise the following, (11) Edges and other sharp intensity transitions, such as noise, in an image contribute significantly to the high frequency content of its Fourier transform. (12) The drawback of the ideal low-pass filter is that the filtering result has obvious ringing artifacts. -
Video Compression Optimized for Racing Drones
Video compression optimized for racing drones Henrik Theolin Computer Science and Engineering, master's level 2018 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Video compression optimized for racing drones November 10, 2018 Preface To my wife and son always! Without you I'd never try to become smarter. Thanks to my supervisor Staffan Johansson at Neava for providing room, tools and the guidance needed to perform this thesis. To my examiner Rickard Nilsson for helping me focus on the task and reminding me of the time-limit to complete the report. i of ii Video compression optimized for racing drones November 10, 2018 Abstract This thesis is a report on the findings of different video coding tech- niques and their suitability for a low powered lightweight system mounted on a racing drone. Low latency, high consistency and a robust video stream is of the utmost importance. The literature consists of multiple comparisons and reports on the efficiency for the most commonly used video compression algorithms. These reports and findings are mainly not used on a low latency system but are testing in a laboratory environment with settings unusable for a real-time system. The literature that deals with low latency video streaming and network instability shows that only a limited set of each compression algorithms are available to ensure low complexity and no added delay to the coding process. The findings re- sulted in that AVC/H.264 was the most suited compression algorithm and more precise the x264 implementation was the most optimized to be able to perform well on the low powered system. -
Filter Effects and Filter Artifacts in the Analysis of Electrophysiological Data
GENERAL COMMENTARY published: 09 July 2012 doi: 10.3389/fpsyg.2012.00233 Filter effects and filter artifacts in the analysis of electrophysiological data Andreas Widmann* and Erich Schröger Institute of Psychology, University of Leipzig, Leipzig, Germany *Correspondence: [email protected] Edited by: Guillaume A. Rousselet, University of Glasgow, UK Reviewed by: Rufin VanRullen, Centre de Recherche Cerveau et Cognition, France Lucas C. Parra, City College of New York, USA A commentary on unity gain at DC (the step response never FILTER EFFECTS VS. FILTER ARTIFACTS returns to one). These artifacts are due to a We also recommend to distinguish between Four conceptual fallacies in mapping the known misconception in FIR filter design in filter effects, that is, the obligatory effects time course of recognition EEGLAB1. The artifacts are further ampli- any filter with equivalent properties – cutoff by VanRullen, R. (2011). Front. Psychol. fied by filtering twice, forward and back- frequency, roll-off, ripple, and attenuation 2:365. doi: 10.3389/fpsyg.2011.00365 ward, to achieve zero-phase. – would have on the data (e.g., smoothing With more appropriate filters the under- of transients as demonstrated by the filter’s Does filtering preclude us from studying estimation of signal onset latency due to step response), and filter artifacts, that is, ERP time-courses? the smoothing effect of low-pass filtering effects which can be minimized by selection by Rousselet, G. A. (2012). Front. Psychol. could be narrowed down to about 4–12 ms of filter type and parameters (e.g., ringing). 3:131. doi: 10.3389/fpsyg.2012.00131 in the simulated dataset (see Figure 1 and Appendix for a simulation), that is, about an CAUSAL FILTERING In a recent review, VanRullen (2011) con- order of magnitude smaller than assumed. -
Destriping of Landsat MSS Images by Filtering Techniques
Destriping of Landsat MSS Images by Filtering Techniques Jeng-Jong Panl TGS Technology, Inc., EROS Data Center, Sioux Falls, SD 57198 Chein-I Chang ., Department of Electrical Engineering, University of Maryland, Balhmore County Campus, Balhmore, MD 21228-5398 ABSTRACT: The removal of striping noise encountered in the Landsat Multispectral Scanner (MSS) images can be gen erally done by using frequency filtering techniques. Frequency do~ain filteri~g has, how~ver, se,:era~ prob~ems~ such as storage limitation of data required for fast Fourier transforms, nngmg artl~acts appe~nng at hlgh-mt,enslty.dlscon tinuities, and edge effects between adjacent filtered data sets. One way for clrcu~,,:entmg the above difficulties IS, to design a spatial filter to convolve with the images. Because it is known that the,stnpmg a.lways appears at frequencies of 1/6, 1/3, and 1/2 cycles per line, it is possible to design a simple one-dimensIOnal spat~a~ fll,ter to take advantage of this a priori knowledge to cope with the above problems. The desired filter is the type of ~mlte Impuls~ response which can be designed by a linear programming and Remez's exchange algorithm coupled ~lth an adaptIve tec,hmque. In addition, a four-step spatial filtering technique with an appropriate adaptive approach IS also presented which may be particularly useful for geometrically rectified MSS images. INTRODUCTION striping lines, it still suffers from several common probleI?s such as a large amount of data storage needed, for .a fast ~our~er HE STRIPING THAT OCCURS IN THE Landsat Multispectral. -
Locally-Adaptive Image Contrast Enhancement Without Noise and Ringing Artifacts
Locally-adaptive image contrast enhancement without noise and ringing artifacts Citation for published version (APA): Cvetkovic, S. D., Schirris, J., & With, de, P. H. N. (2009). Locally-adaptive image contrast enhancement without noise and ringing artifacts. In Proceedings of the IEEE International Conference on Image Processing (ICIP 2007), October 16-19, 2007, San Antonio, Texas (pp. 557-560). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2007.4379370 DOI: 10.1109/ICIP.2007.4379370 Document status and date: Published: 01/01/2009 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -
Performance Comparison of Ringing Artifact for the Resized Images Using Gaussian Filter and OR Filter
ISSN: 2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 2, Issue 3, March 2013 Performance Comparison of Ringing Artifact for the Resized images Using Gaussian Filter and OR Filter Jinu Mathew, Shanty Chacko, Neethu Kuriakose developed for image sizing. Simple method for image resizing Abstract — This paper compares the performances of two approaches which are used to reduce the ringing artifact of the digital image. The first method uses an OR filter for the is the downsizing and upsizing of the image. Image identification of blocks with ringing artifact. There are two OR downsizing can be done by truncating zeros from the high filters which have been designed to reduce ripples and overshoot of image. An OR filter is applied to these blocks in frequency side and upsizing can be done by padding so that DCT domain to reduce the ringing artifact. Generating mask ringing artifact near the real edges of the image [1]-[3]. map using OD filter is used to detect overshoot region, and then In this paper, the OR filtering method reduces ringing apply the OR filter which has been designed to reduce artifact caused by image resizing operations. Comparison overshoot. Finally combine the overshoot and ripple reduced between Gaussian filter is also done. The proposed method is images to obtain the ringing artifact reduced image. In second computationally faster and it produces visually finer images method, the weighted averaging of all the pixels within a without blurring the details and edges of the images. Ringing window of 3 × 3 is used to replace each pixel of the image. -
Signal Processor, Window Provider, Encoded Media Signal, Method for Processing a Signal and Method for Providing a Window
(19) & (11) EP 2 372 703 A1 (12) EUROPEAN PATENT APPLICATION (43) Date of publication: (51) Int Cl.: 05.10.2011 Bulletin 2011/40 G10L 19/02 (2006.01) (21) Application number: 10175141.0 (22) Date of filing: 02.09.2010 (84) Designated Contracting States: (72) Inventors: AL AT BE BG CH CY CZ DE DK EE ES FI FR GB • Helmrich, Christian GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO 91054, Erlangen (DE) PL PT RO SE SI SK SM TR • Geiger, Ralf Designated Extension States: 90409, Nürnberg (DE) BA ME RS (74) Representative: Burger, Markus et al (30) Priority: 11.03.2010 US 312775 P Schoppe, Zimmermann, Stöckeler & Zinkler Patentanwälte (71) Applicant: Fraunhofer-Gesellschaftzur Förderung Postfach 246 der 82043 Pullach bei München (DE) Angewandten Forschung e.V. 80686 München (DE) (54) Signal processor, window provider, encoded media signal, method for processing a signal and method for providing a window (57) A signal processor for providing a processed rality of window value index values, in order to obtain the version of an input signal in dependence on the input processed version of the input signal. The signal proc- signal comprises a windower configured to window a por- essor also comprises a window provider for providing the tion of the input signal, or of a pre-processed version signal processing window values for a plurality of window thereof, in dependence on a signal processing window value index values in dependence on one or more win- described by signal processing window values for a plu- dow shape parameters.