Image quality assessment by an efficient correlation‐based metric

Lin, Lihui; Chen, Tzong-Jer

doi:10.1002/cpe.5794

“…This means that more structured information exists in this area and random noise is less likely in the image [12]. Based on this premise, the Moran Z value has successfully been applied to present the image spatial features [18, 19,[25][26][27]33]. The Moran statistics used in this work divide the noisy areas in the RE.…”

Section: Z =mentioning

confidence: 99%

A FCM-based image de-noising with spatial statistics pilot study

Chen¹

2022

Preprint

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Image de-noising is an important scheme to make the image visually prominent and obtain enough useful information. To obtain reliable results, many applications had developed for effective noise suppression and received good image quality. This report assumed a residual image consisted of noises with edges produced by subtracting the original and a low-pass filter smoothed image. The Moran statistics was then using to measure the variation of spatial information in residual images and then as a feature data input to FCM. Three clusters pre-assumed for FCM in this work: they are heavy, medium and less noisy areas. The rates of each position partially belongs to each cluster were determined by a FCM membership function. Each pixel in noisy image assumed in de-noising processing that which is a linear combination of product of three de-noised images with membership function in the same position. Average filters with different windows and a Gaussian filter priori applied to this noisy image to make three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance is evaluated and compared to the Bilateral filter and NLM using PSNR and SSIM. The developed scheme is a pilot study on this area. Further future studies needed on the optimized number of clusters and smoother versions used in linear combination.

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“…This means that more structured information exists in this area and random noise is less likely in the image [12]. Based on this premise, the Moran Z value has successfully been applied to present the image spatial features [18,19,[25][26][27]35]. The Moran statistics used in this work divide the noisy areas in the RE.…”

mentioning

confidence: 97%

An FCM-Based Image De-Noising with Spatial Statistics Pilot Study

Chen

2023

Applied Sciences

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Image de-noising is an important scheme that makes an image visually prominent and obtains enough useful information to produce a clear image. Many applications have been developed for effective noise suppression that produce good image quality. This study assumed that a residual image consisted of noise with edges produced by subtracting the original image with a low-pass-filter-smoothed image. The Moran statistics were then used to measure the variation in spatial information in residual images and we then used this information as feature data input into the Fuzzy C-means (FCM) algorithm. Three clusters were pre-assumed for FCM in this work: they were heavy, medium, and less noisy areas. The rates for each position partially belonged to each cluster determined using an FCM membership function. Each pixel in a noisy image was assumed in de-noising processing as a linear combination of the product of three de-noised images with membership functions in the same position. Average filters with different windows and a Gaussian filter were a priori applied to this noisy image to create three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance was evaluated and compared to the bilateral filter and non-local means (NLM) using the peak signal to noise ratio (PSNR) and structure similarity index measure (SSIM). The developed scheme is a pilot study. Further future studies are needed on the optimized number of clusters and smoother versions used in linear combination.

show abstract

A FCM-based image de-noising with spatial statistics pilot study

Chen¹

2022

Preprint

0

View full text Add to dashboard Cite

Image de-noising is an important scheme to make the image visually prominent and obtain enough useful information. To obtain reliable results, many applications had developed for effective noise suppression and received good image quality. This report assumed a residual image consisted of noises with edges produced by subtracting the original and a low-pass filter smoothed image. The Moran statistics was then using to measure the variation of spatial information in residual images and then as a feature data input to FCM. Three clusters pre-assumed for FCM in this work: they are heavy, medium and less noisy areas. The rates of each position partially belongs to each cluster were determined by a FCM membership function. Each pixel in noisy image assumed in de-noising processing that which is a linear combination of product of three de-noised images with membership function in the same position. Average filters with different windows and a Gaussian filter priori applied to this noisy image to make three de-noised versions. The results showed that this scheme worked better than the non-adaptive smoothing. This scheme‘s performance is evaluated and compared to the Bilateral filter and NLM using PSNR and SSIM. The developed scheme is a pilot study on this area. Further future studies needed on the optimized number of clusters and smoother versions used in linear combination.

show abstract

Image quality assessment by an efficient correlation‐based metric

Cited by 3 publications

References 23 publications

A FCM-based image de-noising with spatial statistics pilot study

A FCM-based image de-noising with spatial statistics pilot study

An FCM-Based Image De-Noising with Spatial Statistics Pilot Study

A FCM-based image de-noising with spatial statistics pilot study

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