SLIC Based Digital Image Enlargement

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.Comment: 6 page

A Fully Progressive Approach to Single-Image Super-Resolution

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster

A Comparison Study of Deep Learning Techniques to Increase the Spatial Resolution of Photo-Realistic Images

In this paper we present a perceptual and error-based comparison study of the efficacy of four different deep-learned super-resolution architectures, ESPCN, SRResNet, ProGanSR and LapSRN, all performed on photo-realistic images by a factor of 4x; adapting some of the current state-of-the-art architectures using Convolutional Neural Networks (CNNs). The resultant application and the implemented CNNs are tested with objective (Peak-Signal-to-Noise ratio and Structural Similarity Index) and perceptual metrics (Mean Opinion Score testing), to judge their relative quality and implementation within the program. The results of these tests demonstrate the effectiveness of super-resolution, showing that most network implementations give an average gain of +1 to +2 dB (in PSNR), and an average gain of +0.05 to +0.1 (in SSIM) over traditional Bicubic scaling. The results of the perception test also show that participants almost always prefer the images scaled using each CNN model compared to traditional Bicubic scaling. These findings also present a look into new diverging paths in super-resolution research; where the focus is now shifting from solely error-reduction, objective-based models to perceptually focused models that satisfy human perception of a high-resolution image

SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion

Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images, i.e., yielding edge-blurring effect or unrecognizable for object detectors. To alleviate these issues, we propose a semantic structure-preserving approach for IVIF, namely SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract the structural features of infrared and visible images. Then, we introduce a multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural features of infrared and visible images, while maintaining the consistency of semantic structures between the fusion and source images. Owing to these two effective modules, our method is able to generate high-quality fusion images from pairs of infrared and visible images, which can boost the performance of downstream computer-vision tasks. Experimental results on three benchmarks demonstrate that our method outperforms eight state-of-the-art image fusion methods in terms of both qualitative and quantitative evaluations. The code for our method, along with additional comparison results, will be made available at: https://github.com/QiaoYang-CV/SSPFUSION.Comment: Submitted to IEE