Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Image Filtering with Edge and Corner Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-scale approach to nonuniform diffusion
CVGIP: Image Understanding
What's wrong with mean-squared error?
Digital images and human vision
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Subjective evaluation of spatial resolution and quantization noise tradeoffs
IEEE Transactions on Image Processing
An analysis of binarization ground truthing
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavioral analysis of anisotropic diffusion in image processing
IEEE Transactions on Image Processing
A unified approach to noise removal, image enhancement, and shape recovery
IEEE Transactions on Image Processing
Fuzzy homogeneity approach to multilevel thresholding
IEEE Transactions on Image Processing
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
Fourth-order partial differential equations for noise removal
IEEE Transactions on Image Processing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Selective removal of impulse noise based on homogeneity level information
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Noise removal with Gauss curvature-driven diffusion
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion
IEEE Transactions on Image Processing
Fractional-Order Anisotropic Diffusion for Image Denoising
IEEE Transactions on Image Processing
Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
IEEE Transactions on Image Processing
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Denoising filters are useful for reducing noise; however, they often blur and smear the edges and boundaries, which are necessary for segmenting or locating the objects. In order to overcome above problem, many filters with contrast enhancement capability have been developed, and they have wide applications in related fields. Recently, researchers found that the traditional criteria, such as mean squared error (MSE), signal-to-noise ratio (SNR), are not suitable for evaluating such filters. Due to lack of effective metrics for such tasks, visual inspection by human and some newly proposed image quality assessment (QA) criteria, such as structural similarity (SSIM) index are utilized. However, visual inspection depends on the subjectivity of observers heavily. This paper has proved that evaluating denoising filters is different from image quality assessment, i.e., existing image quality assessment criteria cannot effectively evaluate the performance of denoising filters, especially, of the filters having contrast enhancement capability; and new criteria should be established. Further, it proposes a novel objective and effective assessment criterion, homogeneity mean difference (HMD), to evaluate the performance of the filters since it can describe the textual and structural information and/or the changes in textual and structural information well. We have employed 503 images from three databases to demonstrate the superiority of the proposed metric over the existing ones, and to prove that HMD is an effective and useful metric for assessing denoising filters with/without contrast enhancement, which may find wide applications in image processing and computer vision.