Block Based Enhancement of Satellite Images using Sharpness Indexed Filtering

Mishra, Sandeep; Patra, Radhanath; Pattanayak, Abanikanta; Pradhan, Subhrajit

doi:10.9790/2834-0862024

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…A dynamic wavelet-based algorithm [29] was applied to enhance the mammograms. The Discrete Wavelet Transform (DWT)-based method was used because of its low computational complexity and special transformed domain properties [30].…”

Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

“…The log-energies of the vertical, horizontal, and diagonal sub-bands at each decomposition level were calculated followed by measuring the total log-energy (TLE) of each level. Subsequently, by combining the TLE of each decomposition level [29], the Scalar Sharpness Index (SSI) was calculated. The SSI was later used to estimate the overall sharpness of the images.…”

Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

“…Higher values of SSI were considered as an indicator of higher sharpness of the image. More details on the wavelet-based enhancement algorithm were described by Misra et al [29], who applied the enhancement approach to satellite images. To enhance the mammograms, the number of sub-bands and the image decomposition level were chosen as 3, as we aimed to obtain the horizontal, vertical and diagonal details from the mammograms.…”

Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

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Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

2019

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This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95 . 00 ± 0 . 57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0 . 97 ± 0 . 01 .

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Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

Section: Mammogram Enhancement and Patch Extractionmentioning

confidence: 99%

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Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

2019

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“…Scalar Index is computed as weighted average of the log energies which are computed in the earlier step. The Values of (12), (13) and (14) are stored in LH, HL and HH respectively. The total Log energy at each level is computed as TLEn = ((1-W)*((LH+HL)/2)) + (W*HH) (15) Here the 'W' is the weight and is considered as 0.8 in our work.…”

Section: Block Based Enhancementmentioning

confidence: 99%

“…For example in Figure a. we have taken a hazy underwater image and computed the result with different block sizes. We have done experiments on various block sizes and the optimal block size for an image should be greater than 120 and Less than 150 and moreover it depends upon the haze present in the image [13].…”

Section: Block Based Enhancementmentioning

confidence: 99%

A novel method to detect foreground region using morphological operations with block based enhancement for underwater images

Sudhakara¹,

Meena²

2018

IJET

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Automation of detecting the Foreground Region (FR) or Shape of the object is essential in several computer vision, object recognition applications and poses several challenges in case of underwater images. Although Synthetic Sonar Images produce better quality images scattering of light, color distortion and poor lighting conditions are the few characteristics that effects the natural scene of the captured image. A novel technique for extracting the foreground region from a low quality underwater image is presented in this paper. We have decomposed the image in to multiple levels based on discrete wavelet transforms (DWT) for improving the sharpness or to reduce the fogginess in the image in order to get the clear image. Subsequently, to determine the sharpness of the local patches in the image a block based SSI algorithm is presented. Finally, the segmentation is performed by computing the binary gradient mask with the Sobel edge detection algorithm along with morphological operations. The proposed method is fast, extracting the accurate foreground regions and also detect the smallest particles present in the image. The results are qualitatively compared with the improved fuzzy c-means clustering (FCM), Otsu's Threshold and FCM thresholding by considering the static background images.

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