Image Interpolation with Regional Gradient Estimation

Abstract

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

Enhancing Face Recognition Accuracy On Low-Resolution Databases Using Interpolation Techniques And Feature Extraction Techniques

“…The images are in JPG format, and the size of each image is 92×112 pixels with 256 grey levels for representing the pixel value. In general, in edge-based techniques [12,14], after detecting the edges and their directions, the values of the pixels located in the IH(2i, 2j) position indicated by the diagonal arrows in Figure 2c are calculated first. This calculation generally depends on whether the IL(i, j) pixel is an edge pixel with a diagonal angle.…”

“…They utilized an Image Enhancement Network (IEN) and a Face Exchange Module (FEM) to enhance image quality and preserve attributes, demonstrating superior performance compared to existing methods. Qiye Lian et al (2022) [12] presented VLC-FIQA, an unsupervised Face Image Quality Assessment method that quantifies the importance of pixels in a face image and computes the variation of importance as a measure of image quality. Their approach outperformed state-of-the-art methods on LFW, offering a promising solution for face recognition systems.…”

Image interpolation with spiking neural network based pixel similarity

“…IEDI [25], CGI [26], CED [27], PCI [28], WTCGI [29], and GEI [30] are some of the edge-based interpolation techniques proposed in recent years. In the IEDI [25] technique, the edge pixels of the images are detected using an adaptive edge filter.…”

“…The WTCGI [29] technique combines wavelet transform and CGI structure and is applied to the interpolation of images used in the robotics industry. The GEI [30] technique, developed in 2022, performs regional gradient estimation. This approach has shown successful results.…”