Edge enhancement

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

Apparatus for converting and processing input bi-level image data and faithfully rendering tones, colors, and edges

Abstract: An image recorder for converting input bilevel image data to multilevel data, executing various kinds of image processing with the mutlilevel data, and then recording the processed multilevel data. The image recorder converts input bilevel image data to M-level data (M being equal to or greater than 2), then requantized to N-level data, and then fed to a printer capable of recording data in three or more levels. The bilevel-to-multilevel conversion is effected in matching relation to the pattern of the input bilevel data. A plurality of bilevel-to-multilevel conversion means of different kinds are provided. The bilevel-to-multilevel conversion system is changed on the basis of the kind of input bilevel data. The M-level data is subjected to edge enhancement, magnification change, and gamma correction. Color image data produced by color separation are processed color by color and then fed to a printer.

Adaptive unsharp masking method based on region segmentation

Abstract: A adaptive unsharp masking method based on region segmentation is presented aiming at the defects of linear unsharp masking. The input image is divided into homogeneous areas, medium contrast areas and large contrast areas by the local variance of image pixels. Base on the area type to which image pixel (x,y) belongs, the local activity gain factor α(x,y) and the desired output local activity Hd(x,y) are determined adaptively, and the enhancement factor K(x,y) is derived. A X_ray chest image is processed with the proposed method and other unsharp masking methods. The results show that good capabilities of edge enhancing and noise suppressing are achieved by the proposed method.

Pixel-Level Non-local Image Smoothing With Objective Evaluation

Abstract: Recently, imagesmoothing has gained increasing attention due to its prerequisite role in other image processing tasks, e.g., image enhancement and editing. However, the evaluation of image smoothing algorithms is usually performed by subjective observation on images without corresponding ground truths. To promote the development of image smoothing algorithms, in this paper, we construct a novel Nankai Smoothing (NKS) dataset containing 200 images blended by versatile structure images and natural textures. The structure images are inherently smooth and naturally taken as ground truths. On our NKS dataset, we comprehensively evaluate 14 popular image smoothing algorithms. Moreover, we propose a Pixel-level Non-Local Smoothing (PNLS) method to well preserve the structure of the smoothed images, by exploiting the pixel-level non-local self-similarity prior of natural images. Extensive experiments on several benchmark datasets demonstrate that our PNLS outperforms previous algorithms on the image smoothing task. Ablation studies also reveal the work mechanism of our PNLS on image smoothing. To further show its effectiveness, we apply our PNLS on several applications such as semantic region smoothing, detail/edge enhancement, and image abstraction. The dataset and code are available at https://github.com/zal0302/PNLS .

Real-time non-linear spatial filtering with a leaky OASLM

Abstract: In this paper we demonstrate image processing techniques such as edge enhancement and phase contrast by using an optically addressed liquid crystal spatial light modulator (OASLM) in the frequency plane of an optical 4f processor. The transfer function of the device is derived on the basis of the Jones formalism. Faced with a lack of a general theory for such non-linear optical processors, we show that an analogy to the propagation of optical pulses in fibres is helpful for the understanding of the image processing operation.