Improved Content-Color-Dependent Screening (CCDS): Adaptive Bilateral Filtering and Color-Aware Sobel Edge Detector

2021 ◽  
Vol 2021 (16) ◽  
pp. 252-1-252-7
Author(s):  
Yang Yan ◽  
Jan P. Allebach
Keyword(s):  
Digital Image ◽  
Bilateral Filter ◽  
Bilateral Filtering ◽  
Segmentation Method ◽  
Edge Detector ◽  
Number Of Clusters ◽  
Image Segment ◽  
Continuous Tone

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

Study of Segmentation Threshold Based on Wavelet Transform

2013 ◽  
Vol 756-759 ◽  
pp. 3855-3859
Author(s):  
Jian Yi Li ◽  
Hui Juan Wang
Keyword(s):  
Wavelet Transform ◽  
Digital Image ◽  
Region Growing ◽  
Detection Methods ◽  
Mathematical Tool ◽  
Matlab Simulation ◽  
Segmentation Method ◽  
Transform Method ◽  
Threshold Segmentation ◽  
Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

Using images of noise to estimate image processing behavior for image quality evaluation

2021 ◽  
Vol 2021 (9) ◽  
pp. 217-1-217-6
Author(s):  
Norman L. Koren
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Quality Evaluation ◽  
Bilateral Filter ◽  
Important Application ◽  
Maximum Effect ◽  
Bilateral Filtering ◽  
Fine Detail ◽  
Flat Field ◽  
Insight Into

Noise is an extremely important image quality factor. Camera manufacturers go to great lengths to source sensors and develop algorithms to minimize it. Illustrations of its effects are familiar, but it is not well known that noise itself, which is not constant over an image, can be represented as an image. Noise varies over images for two reasons. (1) Noise voltage in raw images is predicted to be proportional to a constant plus the square root of the number of photons reaching each pixel. (2) The most commonly applied image processing in consumer cameras, bilateral filtering [1], sharpens regions of the image near contrasty features such as edges and smooths (applies lowpass filtering to reduce noise) the image elsewhere. Noise is normally measured in flat, uniformly-illuminated patches, where bilateral filter smoothing has its maximum effect, often at the expense of fine detail. Significant insight into the behavior of image processing can be gained by measuring the noise throughout the image, not just in flat patches. We describe a method for obtaining noise images, then illustrate an important application— observing texture loss— and compare noise images for JPEG and raw-converted images. The method, derived from the EMVA 1288 analysis of flat-field images, requires the acquisition of a large number of identical images. It is somewhat cumbersome when individual image files need to be saved, but it’s fast and convenient when direct image acquisition is available.

Probabilistic, Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement

2020 ◽  
Vol 20 (02) ◽  
pp. 2050010
Author(s):  
U. A. Nnolim
Keyword(s):  
Image Enhancement ◽  
Fractional Order ◽  
Bilateral Filter ◽  
Experimental Results ◽  
Bilateral Filtering ◽  
Image Intensity ◽  
Multi Scale ◽  
Global Contrast ◽  
Color Enhancement ◽  
Color Cast

This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.

Detecting Eyelash and Reflection for Accurate Iris Segmentation

2003 ◽  
Vol 17 (06) ◽  
pp. 1025-1034 ◽  
Author(s):  
Wai-Kin Kong ◽  
David Zhang
Keyword(s):  
Statistical Model ◽  
Iris Recognition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Edge Detector ◽  
Recognition Error ◽  
Weak Reflection

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

Analysis on Adjusting Effect and Spectral Characteristics of Digital Image Sampling

2014 ◽  
Vol 539 ◽  
pp. 471-474
Author(s):  
Hai Ying Liu
Keyword(s):  
Digital Image ◽  
Hot Spot ◽  
Modern Art ◽  
Spectral Characteristics ◽  
Image Process ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Image Sampling ◽  
Low Pass ◽  
Continuous Tone

When it comes to the digitized image, it is a process of converting analog image of continuous tone which has been sampled and quantized into digital image. The application of digital technology in modern art has become one of the hot spot in this field. First of all, this paper undertakes the digital image process of image. According to the filtering properties of the Dirac function, this paper analyzes the two-dimensional sampling principle of digital image. Based on this, the relationship between image spectrum before sampling and after sampling is compared and analyzed according to the related properties of Fourier transform. And then it is obtained that it is concluded that the ideal low-pass filter can make the sample undistorted. By further analyzing the error of the sampling value quantification processing, the rebuilt best quantitative values of image can be obtained. Thats to say, the reconstruction of digital image is the inverse process of image sampling. To a certain extent, it provides scientific theoretical basis for the integration of digital image in modern art design.

scholarly journals Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

2011 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
Ika Purwanti Ningrum ◽  
Agfianto Eko Putra ◽  
Dian Nursantika
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Color Image ◽  
Median Filter ◽  
Bilateral Filter ◽  
Filter Method ◽  
Noise Filtering ◽  
Original Image ◽  
Average Filter

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper

Sign in / Sign up

Export Citation Format

Share Document