Image Sharpening by DWT-Based Hysteresis

Haq, Nuhman ul; Hayat, Khizar; Noreen, Neelum; Puech, William

doi:10.1007/978-3-642-23687-7_39

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…9,10 Edge detail improvement in an image is a process of extracting high frequency information from the image and then adding this details to the blurred image. 11 The UM applied to different types of radiological images, will signi cantly improve the level of diagnosis and appropriate treatment. 6 This manipulation may in fact degrade the image quality by introducing artifacts such as overshoot that can lead to misdiagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Residual Based Adaptive Image Sharpening Scheme

Lu¹,

Chen²

2022

Preprint

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The sharpening is a scheme applied to highlight the intensity transitions in an image. This enhancement increases edge acutance and significantly improves the overall sharpness of processed images. Image sharpening is of enormous importance in medicine for diagnosis. Noise overshoot and over-sharpening effects occur in classical sharpening algorithms. The adaptive approach skips the noise producing a better sharpening scheme. Abrupt pixel value changes, if adequately emphasized, can be utilized effectively in the sharpening process. Since the residual image contains noise and edges, the bigger the filter size the greater the number of edges left in the residual image. Two residual images are produced first with different filter sizes. There is a relationship between these two residuals. A simple regression and standard deviation (±σ) is measured using these residuals. This linear equation is an expectation and the standard deviation is the difference between two residuals. One deviation (±σ) is proposed as the threshold. The points contained edges if the residual value is measured using a larger sized filter off ± σ than those residual using a smaller sized filter. A sensitive filter is adaptively applied to the image on those locations. It has been demonstrated that the proposed approach yields better results than the global filter as well as previous work on the P calculation results. A sharpening scheme without latent image noise amplification is useful for pattern recognition and machine-learning. In the future this scheme will be used to pre-process image segmentation.

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Section: Introductionmentioning

confidence: 99%

A Residual Based Adaptive Image Sharpening Scheme

Lu¹,

Chen²

2022

Preprint

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“…This is a computer vision and image processing scheme [Cao, G. et al, 2011]. Image sharpening can be defined as the improvement of apparent sharpness in an image that is supposed to be blurred [Haq N. et al, 2011]. Unsharp masking-based schemes have been widely used in increasing the sharpness of images.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Image Sharpening Scheme

Chen¹

2019

International Conferences Interfaces and Human Computer Interaction 2019; Game and Entertainment Technologies 2019; And Compute

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Image enhancement is one important process for digital images. This processing gives a better subjective visual appearance. The contours and texture are key features that contain important information essential to the visual quality aspect. Adaptive image enhancement schemes are proven better than global methods. An adaptive image sharpening method based on spatial statistics is proposed in this report to effectively sharpen image structures. A noisy Lena was pre-filtered using a low-pass filter to produce a blurred Lena. The residual image was obtained using average filtering and then subtracted from the original image. The high-pass residual image should be a combination of structure and noise. The autocorrelation of each pixel is calculated on the residual image and the image then is sharpened using a nimble filter based on the adaptive autocorrelation values. The PFOM results show that the similarity of adaptive sharpening is better than the global scheme. The proposed method will be further developed and applied to improve image acuity, de-noising and image quality improvement.

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Sharpness Improvement for Medical Images Using a New Nimble Filter

Al-Ameen

2018

3D Res

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Image Sharpening by DWT-Based Hysteresis

Cited by 4 publications

References 6 publications

A Residual Based Adaptive Image Sharpening Scheme

A Residual Based Adaptive Image Sharpening Scheme

An Adaptive Image Sharpening Scheme

Sharpness Improvement for Medical Images Using a New Nimble Filter

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