An improved Image Interpolation technique using OLA e-spline

Image upscaling aims to increase the resolution and size of a low resolution (LR) image in order to generate a high resolution (HR) image of high frequency (HF). There are several polynomial methods for obtaining a sharpened, upscaled HR image. The interpolated pixel is measured using a weighted average of the neighboring pixels within the image grid that blur at HF regions in these methods. Edge degradation is also caused by other edge-directed and learning-based upscaling methods, which produce blurring artifacts. A novel approach is proposed to fill these gaps. Using the concept of unsharp masking (USM), the LR image is blurred adaptively based on the region’s local variance. The sharpened high pass filtered (HPF) image is then obtained by subtracting the adaptively blurred image from the LR image. According to USM, the HPF image is combined with the LR image via a gain factor optimized using the cuckoo search (CS) algorithm. To compensate for the loss caused by upscaling, this pre-processing step is performed prior to interpolation. Aside from that, the edge of the B-spline interpolated image is detected and expanded. Edge expansion of the upscaled image is performed to further restore the HF details and reduce zigzag artifacts introduced by upscaling while also preserving the edge boundary. The proposed method outperforms the Lanczos, Bicubic, and Bilinear schemes in terms of peak signal to noise ratio (PSNR) gain of 3.475, 8.3839, and 8.075 dB, respectively. In terms of performance, this method outperforms state-of-the-art techniques both objectively (PSNR and SSIM) and subjectively (visual quality).