A New Efficient Adaptive Spatial Filter for Image Enhancement

Halder, Amiya; Shekhar, Sandeep; Kant, Shashi; Mubarki, Musheer Ahmed; Pandey, Ankita

doi:10.1109/iccea.2010.55

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Multi-frame detection utilizes the continuity between image sequences; this can suppress the image background with strong correlation and enhance the target area with obvious changes [6]. Currently, the commonly used multi-frame detection algorithms include three-dimensional filtering [7], multi-level hypothesis testing [8], and a new efficient adaptive spatial filter [9]. Compared with multi-frame detection algorithms, single-frame detection has the advantages of good real-time performance and fast response.…”

Section: Introductionmentioning

confidence: 99%

Infrared Weak and Small Target Detection Based on Top-Hat Filtering and Multi-Feature Fuzzy Decision-Making

2022

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Infrared weak and small target detection in a complex background has always been a research hotspot in the fields of area defense and long-range precision strikes. Among them, the single-frame infrared weak and small target detection technology is even more difficult to study due to factors such as lack of target motion information, complex background, and low signal-to-noise ratio. Aiming at the problem of a high false alarm rate in infrared weak and small target detection caused by the complex background edges and noise interference in infrared images, this paper proposes an infrared weak and small target detection algorithm based on top-hat filtering and multi-feature fuzzy decision-making. The algorithm first uses the multi-structural element top-hat operator to filter the original image and then obtains the suspected target area through adaptive threshold segmentation; secondly, it uses image feature algorithms, such as central pixel contrast, regional gradient, and directional gradient, to extract the feature information of the suspected target at multiple scales, and the fuzzy decision method is used for multi-feature fusion to achieve the final target detection. Finally, the performance of the proposed algorithm and several existing comparison algorithms are compared using the measured infrared sequence image data of five different scenarios. The results show that the proposed algorithm has obvious advantages in various performance indicators compared with the existing algorithms for infrared image sequences in different interference scenarios, especially for complex background types, and has a lower performance under the condition of ensuring the same detection rate and false alarm rate and in meeting the real-time requirements of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Infrared Weak and Small Target Detection Based on Top-Hat Filtering and Multi-Feature Fuzzy Decision-Making

2022

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“…The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image. Frequency domain processing techniques are based on modifiying the fourier transform of an image [1] [2].…”

Section: Introductionmentioning

confidence: 99%

“…The method used to impulse noise reduction commonly is a spatial filter method that works with statistical principles such as mean / average filter, median filter, minimum filter, maximum filter, and mode filter [2][3][4][5][6]. In recent years, many studies have developed the median filter method such as hybrid median filters, adaptive median filters, multi-level median filter, etc [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Modified Multilevel Median Filter for Noise Reduction

Hariyanto

Siahaan

Aryza

2019

IJSRST

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Digital image enhancement is efforts to improve the quality of a declining image and one of the causes of the decline in the quality of digital images is the emergence of spots called noise. Median filter is one method that is widely used and developed to digital images noise reduction. In this paper, we conducted an experiment study to reduce noise using a standard multilevel median filter and a modified multilevel median filter. Further, we measured the images filtered quality using MSE and PNSR to find out the advantages of both methods.

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“…It calculates a threshold value in an image and replaces the pixel data if and only if the difference between the highest pixel intensity and the lowest pixel intensity is less than the threshold value. The result is an enhanced image with reduced noise and better sharpness [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Non-linear Filtering Techniques based on Quantitative Metrics using Different Images

Sujatha¹,

Vijayarajan²,

Gupta³

et al. 2012

IJCA

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Image filter is the process of removing various types of noise from the images. The ensuant image is an information rich image than the original input image. The filtering finds its application in many fields from medical imagery, face detection, robot navigation, object detection, aircraft maintenance to image enhancement and image restoration. In the field of medical sciences the filter serves the purpose of image enhancement for efficient disease diagnosis, in aircraft maintenance for the purpose of detection of faults during takeoff, in case of face detection, object recognition and robot navigation used for object detection. This paper uses different quantitative metrics to analyze the result of different filtering techniques on an image. Initially, well known registered images from various aspects of science and nature are taken such as one image ct.jpg from medical sciences, two images Lighthouse.jpg, Penguins.jpg of natural scenery, two images of faces Koala.jpg, lena.jpg and a picture of naturally grown flowers Tulips.jpg are taken as input. Filtering techniques namely Median Filter (MF), Adaptive Filter (AF), New Adaptive Median Filer (NAMF), New Adaptive Spatial Filter (NASF), Edge Preserving Smooth Filter (EPSF) are applied on them. Further the filtered images are analyzed using five quantitative metrics such as Entropy (EN), Standard Deviation (SD), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Mean Absolute Error (MAE). From the experimental result and the corresponding metrics used we observed that the resultant image is more informative than the original source images.

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A New Efficient Adaptive Spatial Filter for Image Enhancement

Cited by 8 publications

References 6 publications

Infrared Weak and Small Target Detection Based on Top-Hat Filtering and Multi-Feature Fuzzy Decision-Making

Infrared Weak and Small Target Detection Based on Top-Hat Filtering and Multi-Feature Fuzzy Decision-Making

Experimental Study of Modified Multilevel Median Filter for Noise Reduction

Analysis of Non-linear Filtering Techniques based on Quantitative Metrics using Different Images

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