The goal of image enhancement is to improve specific features or details of an image and enhance its overall visual quality. We introduce a novel image enhancement algorithm based on block-rooting processing combined with multi-scale exposure image fusion. The proposed method integrates both local and global transform domain-based feedback mechanisms for imaging applications. The core concept of the local alpha-rooting method involves applying it to disjoint blocks of varying sizes, followed by the decomposition of the weight map and multi-scale enhanced images into Gaussian and Laplacian pyramids. Fusion is achieved by multiplying the multi-scale images and their corresponding weights. A new stage is introduced to obtain a local-global estimate of high-contrast images, which is also employed in the general artificial fusion model. Computer simulations conducted on image datasets demonstrate that the new enhancement algorithm outperforms state-of-the-art techniques.
Automatic restoration of damaged or missing pixels is a key problem in image reconstruction for various applications such as retouching, image restoration, image coding, and computer vision. This paper presents a novel approach for reconstructing texture and edge regions, focusing on achieving fine detail in image completion. The proposed method employs spatial reconstruction based on a geometric model, incorporating contour and exemplar-based texture analysis. We propose a technique for restoring object boundaries in images by constructing composite curves using cubic splines and anisotropic gradients. The shape-dependent gradients utilize the distinct forms in the structural pattern to encode both textural and contour information. Additionally, we search for similar patches, fuse them, and apply a deep neural network. We evaluate our model end-to-end on publicly available datasets, demonstrating that it outperforms current state-of-the-art techniques both quantitatively and qualitatively.
The article presumes a data processing algorithm that improves the accuracy of recognition of radio-electronic components in devices for automated installation. The paper proposes the use of a multicriteria filtering method that allows you to automatically change the smoothing coefficient. Varying the coefficient allows both reducing the noise component and preserving the boundaries of the radio elements without blurring. In order to enhance the contours of objects, a data simplification method is applied using the technique of reducing the range of clusters of color gradient histograms while preserving the shapes of objects. At the stage of detecting the boundaries of the elements and forming the structure of the elements of the radio component base, a modified one-dimensional two-criteria method is used. The combined analytical approach allows detection of the boundaries of radioelements and increases the productivity of the process. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors fixed at various magnifications with a resolution of 1024x768 (8 bit, color image, visible range) are used. Images of simple shapes are used as analyzed objects
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