Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

ELM-based spectral-spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets

Abstract: As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). However, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy, we propose a novel spectral-spatial information integrating scheme for pixel-wise kernel ELM-based classifier. In particular, we show that a spatial bilateral filtering information on spectral band-subsets can significantly improve the accuracy of the pixel-wise kernel ELM based classifier. The benefits of the proposed method are twofold: 1) spectral structural similarity guided band-subsets partition and 2) incorporating the spectral-spatial information by bilateral filtering. Experiments on the widely used real HSI demonstrate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy and low computational cost.

Efficient shadow detection by using PSO segmentation and region-based boundary detection technique

Abstract: The presence of shadows in satellite images is inevitable, and hence, shadow detection and removal has become very essential. In this paper, a shadow detection algorithm based on PSO has been used to identify shadows in very high-resolution satellite images. The image is first preprocessed using a bilateral filter to eliminate the noise followed by which PSO-based shadow segmentation is used to segment the shadow regions. Canny edge detection is done to identify the edges of the objects in the image. The results of the edge detection and segmentation are combined using a logical operator to generate the final shadow segmented image with well-defined boundaries. The accuracy is validated using precision and recall parameters.

High dynamic range image tone mapping and retexturing using fast trilateral filtering

Abstract: Using fast trilateral filtering we present a novel tone mapping and retexturing method for high dynamic range (HDR) images. Our new trilateral filtering-based tone mapping is about seven to ten times faster than that in [3]. Firstly, a novel tone mapping algorithm for HDR images is presented. It is based on fast bilateral filtering and two newly developed filters: the quasi-Cauchy function kernel filter and the fourth degree Taylor polynomial kernel filter. Secondly, a new gradient-based image retexturing method is introduced, which consists of three steps: 1) converting HDR images into low dynamic range (LDR) images using our fast trilateral filtering-based tone mapping method; 2) recovering the gradient luminance maps for the region to be retextured; 3) reconstructing the final retextured image by solving the Poisson equation. The proposed approach is suitable for HDR image tone mapping and retexturing, and experimental results have demonstrated the satisfactory performance of our method.

Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising.

Abstract: Images captured underwater are often characterized by low contrast, color distortion, and noise To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement In the proposed framework, Gaussian filter and Bilateral filter are respectively employed to decompose the high-frequency and low-frequency components Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component Accordingly, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy The proposed ACCE is a new adaptive variational framework implemented in the HSI color space, in which we design a Gaussian weight function and a Heaviside function to adaptively adjust the role of data item and regularized item Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure Experimental results demonstrate that our strategy is effective in color correction, visibility improvement, and detail revealing Comparison with state-of-the-art techniques also validate the superiority of propose method Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene

Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

Abstract: Surveillance videos of coal mining faces have close relation to the safety of coal miners and mining efficiency. However, surveillance videos are always disturbed by some severe conditions such as atomization, low illumination, glare, and so on. Therefore, this paper proposed a hybrid algorithm (SSR-BF) based on the integration of single-scale Retinex (SSR) and bilateral filtering (BF) to enhance the image quality of surveillance videos. BF was coupled with SSR to reduce the noises and perfect the edge information in the image. The schematic diagram and pseudocode of SSR-BF was designed, and the parameters were set rationally to ensure the enhancement effects through some simulations. Finally, some comparisons with other methods were carried out, and the simulation results demonstrated that the proposed algorithm was superior to others and could be applied to image enhancement for poormonochrome images, especially the surveillance video of a coal mining face.