SLIC Based Digital Image Enlargement

TL;DR

This paper introduces a novel image enlargement method that reduces color bleeding by segmenting homogeneous regions with SLIC and applying higher order interpolation, resulting in better image quality.

Contribution

The paper presents a new approach combining SLIC segmentation and advanced interpolation to improve low-resolution image enlargement and reduce color bleeding artifacts.

Findings

Outperforms bilinear and bicubic interpolation in PSNR

Reduces color bleeding at edges

Enhances overall image quality

Abstract

Low resolution image enhancement is a classical computer vision problem. Selecting the best method to reconstruct an image to a higher resolution with the limited data available in the low-resolution image is quite a challenge. A major drawback from the existing enlargement techniques is the introduction of color bleeding while interpolating pixels over the edges that separate distinct colors in an image. The color bleeding causes to accentuate the edges with new colors as a result of blending multiple colors over adjacent regions. This paper proposes a novel approach to mitigate the color bleeding by segmenting the homogeneous color regions of the image using Simple Linear Iterative Clustering (SLIC) and applying a higher order interpolation technique separately on the isolated segments. The interpolation at the boundaries of each of the isolated segments is handled by using a morphological operation. The approach is evaluated by comparing against several frequently used image enlargement methods such as bilinear and bicubic interpolation by means of Peak Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the proposed method outperforms the baseline methods by means of PSNR and also mitigates the color bleeding at the edges which improves the overall appearance.