Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.

Edge detection and sharpening process for an image

Abstract: A method and device for sharpening detected edges in an image to compensate for a corruption that occurs during the scanning and printing processes Edges are enhanced by increasing the contrast between two sides of an edge region according to the amount of distortion in the image signal at that location Each pixel in the image is analyzed in the context of neighboring pixels in the image to determine the presence of an edge and the degree of sharpening required A filter is applied to adjust the intensity value of pixels in an edge region to correct for distortion and to emphasize the edge The resulting final image contains sharpened edges with little effect on the smooth transition regions of the image

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Abstract: The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

Mutual-Structure for Joint Filtering

Abstract: Previous joint/guided filters directly transfer the structural information in the reference image to the target one. In this paper, we first analyze its major drawback -- that is, there may be completely different edges in the two images. Simply passing all patterns to the target could introduce significant errors. To address this issue, we propose the concept of mutual-structure, which refers to the structural information that is contained in both images and thus can be safely enhanced by joint filtering, and an untraditional objective function that can be efficiently optimized to yield mutual structure. Our method results in necessary and important edge preserving, which greatly benefits depth completion, optical flow estimation, image enhancement, stereo matching, to name a few.

Content-Aware Dark Image Enhancement Through Channel Division

Abstract: The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images—e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images—without introducing artifacts, which is an improvement over many existing methods.

A new fuzzy logic filter for image enhancement

Abstract: This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.