Image Processing for Laser Imaging Using Adaptive Homomorphic Filtering and Total Variation

Abstract

Laser active imaging technology has important practical value and broad application prospects in military fields such as target detection, radar reconnaissance, and precise guidance. However, factors such as uneven laser illuminance, atmospheric backscatter, and the imaging system itself will introduce noise, which will affect the quality of the laser active imaging image, resulting in image contrast decline and blurring image edges and details. Therefore, an image denoising algorithm based on homomorphic filtering and total variation cascade is proposed in this paper, which strives to reduce the noise while retaining the edge features of the image to the maximum extent. Firstly, the image type is determined according to the characteristics of the laser image, and then the speckle noise in the low-frequency region is suppressed by adaptive homomorphic filtering. Finally, the image denoising method of minimizing the total variation is adopted for the impulse noise and Gaussian noise. Experimental results show that compared with separate homomorphic filtering, total variation filtering, and median filtering, the proposed algorithm significantly improves the contrast, retains edge details, achieves the expected effect. It can better adjust the image brightness and is beneficial for subsequent processing.

Improved Weighted Non-Local Mean Filtering Algorithm for Laser Image Speckle Suppression

“…A histogram distribution generally describes the overall distribution of image pixels, as a table representing the number of pixels with a certain value in an image [ 16 ]. In the histogram, there are 256 abscissa values to represent the pixel values (the pixel distribution is mainly concentrated below 120 pixels, so values above 120 are not shown in the graph for a better display), and the vertical axis represents the distribution.…”

“…Filtering algorithms based on the frequency domain include ideal high/low-pass filter [ 12 ], Butterworth high/low-pass filter [ 13 ], and Gaussian high/low-pass filter [ 14 ]. With continuous research by scholars at home and abroad, filtering algorithms are emerging one after the other, such as guided filter [ 15 ] calculated iteratively, homomorphic filter [ 16 ] combining frequency filter and spatial grey transformation, and various fusion filtering algorithms.…”

Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

“…As a result, image processing techniques are developed to reduce or eliminate noise [42]. Noise can also be generated during image transmission and acquisition processes [22], which has sparked interest in the field of image processing [22,[42][43][44][45][46]. As you can see, noise is an interesting topic due to the disruption it causes, which makes it difficult to deal with.…”

“…The random nature of noise is a result of these physical processes. According to the Central Limit Theorem, the sum of a large number of random variables, including noise, tends to follow a Gaussian distribution [22,42,44]. Noise, regardless of its type, can be categorized as additive, subtractive, or mixed (salt and pepper).…”

Imaging Feature Analysis-Based Intelligent Laser Cleaning Using Metal Color Difference and Dynamic Weight Dispatch Corrosion Texture

“…The phase transformation, the deposition of materials, the uncertainty of corrosion distribution, the laser parameters, and the laser working path will affect the cleaning procedure and result. In this study, an SVM [21] is considered to search for suitable laser process parameters under complex working conditions [22]. In the training stage, a variety of image features and laser parameters are used as the input vector, and the qualified or unqualified evaluation result is regarded as the SVM output.…”